Tag: credit risk management

Get the latest from our global experts with these top December headlines, including meeting the demand for digital, increasing consumer expectations, women leading artificial intelligence, and protecting against fraudsters over the holiday shopping season. Investment priorities to meet consumer demand for digital banking In this BAI Banking article, Chris Fletcher, SVP Decision Management & Cloud Services, explores the investment required of financial institutions to transform their use of data and analytics and deliver on credit risk strategies. What’s the proper path for better payments? In context to consumers’ digital expectations post-Covid-19, Progressive Grocer considers the future of payments in food retail and beyond – with contactless payment options already rolling out at a large drug store chain. Wisdom from the women leading the AI industry, with Laura Stoddart of Experian Authority Magazine speaks with Laura Stoddart, Data Scientist, about her career path, her experiences working on ethical AI and using emerging datasets to evaluate risk as well as her thoughts on the future of this industry. #TradeTalks: Increasing consumer demands and expectations Steve Wagner, Global Managing Director of Decision Analytics, joins Nasdaq’s Jill Malandrino to discuss recent research findings on increasing consumer demands and digital expectations, and ongoing considerations for a post-Covid-19 world. A holiday season like no other: What to know to guard your company against fraud Itay Levy, Forbes Councils member and CEO and Co-Founder of Identiq, provides his perspective on the increased preference for online shopping and the need to strike the right balance between customer experience and efforts to mitigate fraud. Stay in the know with our latest insights:

Check out the 10 most popular stories of 2020 that will help to kick-start your 2021. It includes a look back on how trends evolved throughout the past year, which trends will be durable in the new year, and what the global pandemic has taught us about creating meaningful relationships between consumers and businesses. Top 10 list of the best stories in 2020: 10. Model recalibration drives impactful results during constant change Banks have managed through stressed scenarios in the past but none have ever had to predict customer behavior in a pandemic. General indicators of risk or stress didn’t reveal enough about what was going on in customer portfolios. Active model calibration in our current situation had a measurable effect on approvals and expected losses but executives still needed to regain control over disrupted models. Read full article 9. Digitally managing your at-risk customers most impacted by Covid-19 Lenders felt a tremendous amount of pressure this year trying to help reduce the impact of the financial burden Covid-19 put on consumers by supporting payment forgiveness and deferment programs. This made it difficult, though, to understand changes in the credit profile of a previous solvent customer and mobilize their operations teams to service these good but at-risk customers. Read full article 8. The rising need for identity verification Consumers turned to digital when mass closures of businesses prevented in-person transactions. Even as some businesses re-opened with precautions in place, many consumers still felt it was safer to do business online emphasizing the importance of security and identity verification. But while some level of friction invokes a sense of security, too much or unnecessary friction had an adverse effect. Read full article 7. Proactively restructuring debt to help improve customer affordability At the beginning of the year, no one could accurately predict how the world would be impacted by Covid-19 or how long it would last. Customer affordability models shifted into unknown territory and businesses tried to figure out how to assess customer risk in this new context. Lenders relied on the customer data and insights available to them and needed them to work harder at anticipating changes. Read full article 6. Be mindful of these 3 strategies when engaging customers digitally The road to digital was already being paved when the pandemic started but consumers and businesses were pushed there to engage en masse this year. There were practical challenges that needed to be addressed in the short-term, like managing call volume with a remote workforce. But more importantly, it put the spotlight on massive areas in need of modernization, such as the management of liquidity and risk. Read full article 5. Banking trends and opportunities in the post-Covid-19 crisis era This year was marked by adaptation, resilience, and reflection – which can be said for our personal lives – but in the context of the banking industry, it created an opportunity to change or accelerate priorities. Moving operations to the cloud, making sure decision strategies are fit-for-purpose, and applying analytics in a more useful way are some of the stickier trends we’ll likely see continue in 2021. Read full article 4. Why consumer trust in the digital experience is so important in a pandemic era Despite the uncertainty of this past year, one thing remained certain – cultivating customer trust is critical to brand loyalty. Digital customer trust, however, required businesses to consider several specific factors that inform and build trust. Digital adoption was mistakenly considered the most important of those yet being treated fairly, customer recognition, and fraud prevention were stronger signals. Read full article 3. Game changers: Women in Artificial Intelligence Artificial intelligence offers a lot of value, especially when used to better support customers’ financial needs. As more businesses processed huge amounts of data with advanced analytics and AI this year, human oversight was key to ensure transparency and explainability. This “human element” was the inspiration for an article mini-series featuring five women who are making a real difference using AI innovation. Read full article 2. Digital transformation through cloud-first decisioning The credit and fraud risk decision management landscape changed this year – including how the customer journey is being redefined. Mounting consumer expectations for a better digital experience meant the front and back end of a business’ operations were no longer mutually exclusive. Cloud-based applications was the reset needed to move away from functional and product silos to focus on the customer. Read full article 1. Covid-19 as a gateway to fraud Fraudsters are opportunistic which exposed another ugly side of the pandemic throughout the year. As people and businesses moved to digital to engage with one another, criminals exposed weaknesses in the tools, processes, and systems used to protect those interactions. Investment in fraud prevention was already on the rise but steadily increased throughout the year as new fraud trends emerged. Read full article

  The need for advanced technologies, such as artificial intelligence, has surged in the wake of Covid-19. The strain of the pandemic on businesses and economies has created tension in operational models requiring a quick and dramatic response to this digital disruption. As transformation efforts continue, there are several considerations for the growing field of AI – including ethical AI, the need for diversity and gender balance, and striving to be consciously unbiased. This final post in our “Game Changers: Women in AI” series takes a deep dive into AI careers. Our experts share important lessons on how to thrive, including having mentors and sponsors, staying relevant with new related skills, understanding problems to be solved, believing in yourself and actively seeking growth opportunities. New to the Game Changers: Women in AI series? Read Part 1 - Game Changers: Women in AI Read Part 2 - Game Changers: Women in AI   Q: What advice would you give to help other women in AI thrive? He: "I would suggest being brave. Don't be afraid of trying new things. Sometimes we fear we cannot do something, but once you try it you find it’s not very difficult, you can do it. You can do it very well. So, I think the first thing is just try it. Don’t be afraid of making mistakes. If you go this route, be confident. Women are very smart and competitive, but they may not recognize how good they are. Also, if you find that you may be interested in this area, find resources and see if this is something you want to dedicate yourself to. There are a lot of options online. Even a lot of the universities now offer their courses online. People also share code online, so there are lots of good resources to help you explore and start learning. Overall, remember to believe you can do it." Kazmi: "For everyone that wants to try AI, or if you’re already working here and want to remain in the industry and do good work, you have to keep yourself relevant — learning and keeping yourself updated with the newest research that's happening. There is no end to learning in this field. At the same time, you need to have business knowledge to truly understand a business problem statement and convert that to a data science problem statement, and then start developing solutions for it. I really think that women can be strong contributors in this regard by leveraging their management and analytical skills to bridge the gap between the two areas." Kung: "I think we all need to be ourselves and respect ourselves. You need to have a goal and work hard for it. I think it is the same for anyone who wants a successful career. You need to set a goal and work hard for it and you will achieve it. Really, it's all about working hard. Also, my experience in AI has included a lot of brilliant women, so I never really felt like this is a job for men vs. women. The truth is we want more people to understand what we are doing – that there are many great things we can do with data. It is not something to fear. It’s not this magical thing. It is statistics. It is computing. It's coding. It's everything good." Peters: "It's so important to reach out and look for both mentors and sponsors, and this can be at any age. Mentors are our sounding boards to help with career development. There's some overlap with sponsors, who are opening the doors and speaking about you on your behalf in order to accelerate the track to the next place that you want to be. Mentors and sponsors are good starting from a very young age – and I think that’s a critical aspect of bringing more women along. Find these folks, make those connections, nurture those relationships, and have those mentors and sponsors. I really think that's a key aspect. Also, women do not necessarily need their network to be all women. You need to find the best people positioned to help you in your journey." Stoddart: "Having a mentor is good, especially someone who's more senior in your target field. And, it doesn't necessarily have to be somebody who you're working with or somebody who's your boss. They can be from academia or a different company. It's nice to have the outside perspective. It’s also helpful to network – I’m using virtual events now. I’ve met a lot of women in data science through activities outside of my current role. There are so many opportunities beyond your day to day job. I try to have a few things going at once -- I'll mentor somebody, I'll have a side project or volunteering, and my full-time job as well. For example, for the social enterprise I'm working with, I'm getting experience forecasting. It's nice to give back, but it also makes you a stronger data scientist to work on these different projects." Q: Is there a person or experience you are grateful towards that helped set you on the path to where you are today? He: "First, after graduation, I got a job in transaction analytics, detecting fraud transactions in credit cards. Essentially, it has the same goal as other projects, understanding human behavior from large amounts of data. That's what amazed me and kind of drove me into this direction. After that, I got the job here at Experian and I was exposed to a lot of great innovations and projects." Kazmi: "In the eight and a half years I’ve been in the AI industry, I’ve had the opportunity to work with multiple organizations across different domains. Through this diverse experience, I’ve met and worked with women from different backgrounds both as leaders, as well as colleagues. I’ve seen successful women leaders from all walks of life – from different educational backgrounds, whether from computer science, engineering, mathematics, or economics management, et cetera, or even differing nationalities and ethnicities. It has been impactful to see successful women leaders cutting across industries and localities." Kung: "Professionally, the person that I'm grateful to is my first boss. He was a teacher for me and taught me a lot. Everything that I am today, everything that I do at work, professionally, he was who trained me for it. When I think of the professional Jennifer, I always think of him. I think in my whole career, everyone who was part of my path, they helped me somehow. Maybe in little ways, and maybe in some big ways, they all helped me." Peters: "There are so many people I am grateful to in my career. Overall, where I am today comes down to the opportunities I was given. I had the opportunity earlier on in a prior role to be exposed to big data and frameworks, an exciting precursor to my work with AI. Today, when I think about my work with fraud and identity, AI is such a critical piece of that. And it's becoming increasingly important as we apply these concepts into financial services. I’ve been able to join collaborative and innovative colleagues, fraud experts, in a unified quest to solve the fraud challenge." Stoddart: "I am grateful to the person who brought me into this department. He saw something in me, he understood that I really wanted to learn, and he created a position for me. They were not hiring for a data analyst at the time, so that was really energizing. Also, I don't look for positions that already exist, because if everybody applies for positions that exist, it’s limiting your scope. A lot of the things that I've obtained in my life, it's because I've been a bit brave and asked for it. Even if it's not there on a plate, here I am." Related stories: New Podcast from AI in Business: The evolution of the data business in the age of AI Game Changers: Women in AI (part 1) Game Changers: Women in AI (part 2) Yi He Yi He works as a data scientist in the Experian NA DataLab. She is dedicated to using machine learning and AI to extract information from large amounts of data to identify, understand and help people, and prevent fraud. She aims to bridge online and offline worlds by linking identity data from these unique sources. With a focus on minimizing friction to customers, Yi’s work helps organizations identify synthetic identities to avoid fraudulent applications. Recently, she contributed to a Covid Outlook & Response Evaluator (CORE) Model – a “heat map” of geographic populations across the U.S. most susceptible to severe cases of Covid-19. Deeba Kazmi In her role as a data scientist at the Experian APAC DataLab, Deeba Kazmi is focused on solving business problems with analytics, including the development of consumer and small to medium enterprise credit risk models that leverage alternative data. Deeba is passionately focused on leveraging AI to create solutions that can help address issues faced by developing markets. Most prominently, this work includes her data science leadership contributions to solving a crucial economic and societal problem – financial inclusion. This effort is helping disadvantaged socio-economic consumer groups gain access to vital credit and financial services by leveraging the power of technology to deliver better outcomes. Jennifer Kung Jennifer Kung is an analytics consultant for Serasa Experian Decision Analytics, where she combines her knowledge of financial services with her data analysis expertise. Jennifer aims to harness the power of data through robust, descriptive and predictive analytical solutions to help clients realize the benefits of the massive amounts of data available to them. She recognizes the magnificence in powering discoveries through data analysis and enjoys revealing these capabilities to businesses who can benefit from these robust, yet approachable solutions. Jennifer enjoys knowing that her work helps to simplify and accelerate decisions that consumers rely on at important times in their life. Kathleen Peters Kathleen Peters leads innovation and business strategy for Decision Analytics in North America. As the prior Head of North America Fraud & Identity business, Kathleen is well-recognized as an identity industry innovator, being named a “Top 100 Influencer in Identity” by One World Identity the last two years. As of 2020, Kathleen was named Chief Innovation Officer for Decision Analytics. Kathleen and her team rely on the power of AI to continuously find new ways to solve customer challenges by defining product strategies, new paths to market and investment priorities. Underlying these efforts is a key focus on the ethical use of technology and the need to be consciously unbiased. Laura Stoddart Laura Stoddart is a physicist turned data scientist who works at the Experian DataLab in London. From her first exposure to AI, she recognized how quickly it can have an impact on the world, which has driven her to get and stay involved in the industry – both professionally and personally. Laura’s recent work has focused on ethical AI, having recently contributed to her first paper addressing the removal of bias from models. In addition, she is concentrated on leveraging emerging datasets to evaluate risk. Outside the DataLab, Laura also volunteers her data science skills to good causes such as Bankuet and helps expose others to the world of AI through mentoring.

The relationship with artificial intelligence may have started with robots but its integration into the way people interact with the world today looks very different. AI is in our pockets, our homes, our workplaces, and its pay-off is being realized across many industries, including financial services, e-commerce, telecommunications, streaming services, insurance companies, and more. Though some people and businesses still have reservations about its use. In the next article in our “Game Changers: Women in AI” series, we examine the artificial intelligence debate with arguments against and for its use in our everyday lives, and how it can bring real value to our interactions with businesses – whether it’s preventing fraud, increasing financial accessibility, enhancing the digital experience or supporting public initiatives to prevent the spread of Covid-19. Q: What is your view on criticism of AI or arguments against its use? He: "AI is already all around us and sometimes people don’t even realize it. For example, smart devices remember your preferences, try to understand your behaviors, and help you with reminders, goals, or some other alert. For some, this can feel a little bit scary, like they are collecting information and profiling you. But really, AI is helping people by using large amounts of data to train models and find patterns in the information to solve complicated problems." Kazmi: "Since AI is still so new, every time a product or a change in experience through AI is introduced, there are bound to be reluctancy in adoption and initial failures which lead to opposition. But, to establish the final best product possible, we need understanding between AI research teams and business stakeholders. Take the example of Elon Musk. He has come up with SpaceX and Tesla, but there have been so many failures in their development. Still, the entire world was looking up to these ventures, because these products are something that's going to bring huge positive change." Kung: "People need to keep in mind that AI, and all this data science technology, are just tools to help us. It's not that a machine will replace someone. I’ve heard a lot of people saying, "You create things automatically, and machines will replace our job." That’s not how it is. The truth is, we are creating these kinds of things to help us. It improves our lives by saving our time to focus on other useful things that a machine can’t do." Peters: "It’s helpful to consider what got us here. Years back, people would ask, “Are you ready for big data? Do you have big data?” What we found was that as more data was available, even when managed effectively, we needed ways to consume it and to garner insights from it. This underlying piece drove the need for AI and machine learning. Working with these technologies is critical to harnessing the power of data for what we do, to apply these concepts to fuel significant problems, like stopping fraud." Stoddart: "The topic of bias in AI creeps up in the news. If an algorithm is not checked properly, it could mean a portion of the population isn’t reflected. This stems from assumptions inherent in people. If those writing the code are not diverse, you likely miss out on representing whole groups of people in the wider society. This issue of bias emphasizes the importance of team diversity, of driving success by having opinions challenged and ensuring representation across diverse groups." Q: Is there anything you would like to share that could help alleviate fears and show the public that AI is beneficial? He: "It will lessen fears if we can help people realize there needs to be humans involved. To understand the data, to understand human behavior, everything is about the observation and how you interpret it. It also helps to share the benefits people will realize. For example, AI can improve consumer experiences — such as when filling out an application. It can build bridges between different types of data to supplement the details provided. This reduces the friction felt by the applicant by simplifying the inputs required, which is very useful on wearables and mobile devices." Kazmi: "AI can change the world. If you just look around, data science is part of everything nowadays. And, there's often a solution you benefit from but are not even aware that it has AI embedded in it in some way. It’s important to encourage understanding and acceptance and highlight all the good work that people are doing in this industry. We need to acknowledge and encourage endeavors to further these contributions and progress in the AI industry." Kung: "My concern is that people think “Oh, you just put something in the machine and the machine will tell you what to do." It's not like that. People need to realize a human must analyze the results – what it gives you and what you see. It needs to make sense for their business. The machine will not know what you’re analyzing. It will just run the algorithms that you put in it and it gives you a number. It’s up to people to analyze it." Peters: "Whenever you go into a new and somewhat unexplored area, there will always be different aspects to consider. As researchers, innovators, and developers, we need to be aware of inherent risks and keep an eye on the ethical aspects of technology. This focus helps ensure the thoughtful progression of AI, creating the right guardrails to thwart fraudsters and ill-intentioned individuals and equality by being “consciously unbiased” in the models and systems we are building." Stoddart: "I mentioned the need for diversity to prevent bias. I’m proud to be contributing to a project called “fairness.” It’s about tackling bias in models – using AI to help treat everyone fairly. Our work has enabled people to drill down and properly check attributes to ensure that decisions are fair and not discriminating against a certain group. If it’s not fair, it provides the opportunity to fix it. I believe this will be a really important tool going forward." Q: What examples can you share for how AI can bring goodness to the world? He: "At the very beginning of our latest initiative, we were thinking, “how will this development and innovation help the world?” It was hard to answer until we created different use cases. Currently, we have several meaningful results using AI – linking data to identify a person and deliver the best customer experience and helping detect fraudulent applications using fake or synthetic IDs. We also recently developed a heatmap for predicting Covid-19 severity for more than 3,000 counties in the U.S. We’ve made this tool available to assist public researchers as well as government and policymakers." Kazmi: "I am truly satisfied with the work that I have been doing because it's very exciting to find new ways to have a positive impact. From the day I joined Experian, I've been part of a project called financial inclusion, leading the data science part of it. We are helping people and entities stuck at the lowest level of the financial ladder. This is the beauty of data science, helping consumers and small entities access credit and come out of a vicious cycle, to move up financially, leading to the overall growth of the financially weaker sections of society." Kung: "Within my area of focus, financial services, we can help make life easier and help get things done faster. The important thing is time-saving because we need to get things done quicker. For example, sometimes people try to secure credit and the bank takes too long to give an answer. Or, with a mortgage, there is a lot of paperwork needed. We can use an AI tool to help analyze this paperwork faster, which helps the customer who needs the loan get their home faster." Peters: "Some of the ways that it can bring goodness to the world is where we are just limited by the scale or the speed that we want to move when solving problems based on huge amounts of data, especially in real-time. Where AI can help predict next best actions or best outcomes in a way that usually would require a lot of research or photographic memory. Very relevant today, this applies well to the medical domain, but there are so many areas AI can help us better consume data at our fingertips and predict new innovative areas to explore." Stoddart: "In addition to the fairness project I mentioned, I also use my data science skills volunteering with a social enterprise, helping them obtain the insights they need to determine what food and supplies are most needed at food banks. The insight allows them to prioritize what items to buy in bulk with monetary donations from the public. Usually, food banks are really separated in the UK, so this is a new approach benefitting from advanced technologies." Related stories: Game changers: Women in artificial intelligence (part 1) Impact of technology on changing business operations Forbes: Are we comfortable with machines having the final say? Yi He Yi He works as a data scientist in the Experian NA DataLab. She is dedicated to using machine learning and AI to extract information from large amounts of data to identify, understand and help people, and prevent fraud. She aims to bridge online and offline worlds by linking identity data from these unique sources. With a focus on minimizing friction to customers, Yi’s work helps organizations identify synthetic identities to avoid fraudulent applications. Recently, she contributed to a Covid Outlook & Response Evaluator (CORE) Model – a “heat map” of geographic populations across the U.S. most susceptible to severe cases of Covid-19. Deeba Kazmi In her role as a data scientist at the Experian APAC DataLab, Deeba Kazmi is focused on solving business problems with analytics, including the development of consumer and small to medium enterprise credit risk models that leverage alternative data. Deeba is passionately focused on leveraging AI to create solutions that can help address issues faced by developing markets. Most prominently, this work includes her data science leadership contributions to solving a crucial economic and societal problem – financial inclusion. This effort is helping disadvantaged socio-economic consumer groups gain access to vital credit and financial services by leveraging the power of technology to deliver better outcomes. Jennifer Kung Jennifer Kung is an analytics consultant for Serasa Experian Decision Analytics, where she combines her knowledge of financial services with her data analysis expertise. Jennifer aims to harness the power of data through robust, descriptive and predictive analytical solutions to help clients realize the benefits of the massive amounts of data available to them. She recognizes the magnificence in powering discoveries through data analysis and enjoys revealing these capabilities to businesses who can benefit from these robust, yet approachable solutions. Jennifer enjoys knowing that her work helps to simplify and accelerate decisions that consumers rely on at important times in their life. Kathleen Peters Kathleen Peters leads innovation and business strategy for Decision Analytics in North America. As the prior Head of North America Fraud & Identity business, Kathleen is well-recognized as an identity industry innovator, being named a “Top 100 Influencer in Identity” by One World Identity the last two years. As of 2020, Kathleen was named Chief Innovation Officer for Decision Analytics. Kathleen and her team rely on the power of AI to continuously find new ways to solve customer challenges by defining product strategies, new paths to market and investment priorities. Underlying these efforts is a key focus on the ethical use of technology and the need to be consciously unbiased. Laura Stoddart Laura Stoddart is a physicist turned data scientist who works at the Experian DataLab in London. From her first exposure to AI, she recognized how quickly it can have an impact on the world, which has driven her to get and stay involved in the industry – both professionally and personally. Laura’s recent work has focused on ethical AI, having recently contributed to her first paper addressing the removal of bias from models. In addition, she is concentrated on leveraging emerging datasets to evaluate risk. Outside the DataLab, Laura also volunteers her data science skills to good causes such as Bankuet and helps expose others to the world of AI through mentoring.

The artificial intelligence (AI) market is expected to grow 159% by 2025 to $190.61 Billion, according to Markets and Markets, and there’s considerable value for businesses and consumers. In our July global survey of businesses and consumers, we found that 60% of businesses planned to invest in advanced analytics and AI to better support their customers' financial needs during Covid-19. As more businesses adopt AI, processing their vast amounts of data with advanced analytics for automated decisions, human oversight is and will remain key to ensure transparency and explainability. This “human element” in AI was the inspiration for our latest “game changers” series. We recently sat down with five industry experts to get their view on how AI is making the world a better place, and how its use in financial services can be realized. Yi He, Deeba Kazmi, Jennifer Kung, Kathleen Peters, and Laura Stoddart are visionaries and leaders in data science and innovation making a real difference in how advanced technologies are helping consumers and businesses engage more meaningfully. Q: What excites you most about the AI Industry? He:  "As AI is more involved in our lives, it provides benefits we couldn’t imagine before – such as using your face to unlock your phone security. With the development of AI and machine learning, we can find patterns in data or in behaviors of people to solve complicated problems. That’s really it; helping people make life easier." Kazmi: "The main thing is that AI is not only transforming the way we live and communicate, it's changing the way almost every industry around the world is going to operate. To positively contribute to this growth, it’s not just that you need to learn and then deliver, but to keep innovating and coming up with new solutions that others learn from." Kung: "The technology improvement excites me. Things are getting easier, giving us more time to focus on what really matters. We usually don’t have time to focus on some of these areas because we are used to doing things manually. Now with AI, we have a machine to do a job that is manual, so we can focus on analysis and improvement." Peters: "What’s most exciting for me are ways AI technology can augment human decisions and innovation, in new directions that we historically run out of horsepower for. And, it can be applied to virtually every industry — the ways that it can better help us leverage big data, robotics, the Internet of Things — there are so many directions we can go with AI." Stoddart: "One of the most exciting things about AI is that people benefit from it every day — using social media, or maps to get to the shops, sometimes without even realizing it. And, if you can create an algorithm that can help somebody get credit who previously couldn't, you can have a real impact on the world that actually changes people's lives for the better." Q: What concerns you most about the AI industry? He: "I think the key things are data security and privacy protection. People are more and more sensitive about their information being used and released, which is understandable, and why opportunities exist to opt-out of information being used or sold to third parties. The key is to offer comfort by building in how to secure the data and protect privacy." Kazmi: "There are pros and cons of everything, especially with a stream of faster evolutions in prominent areas affecting our day-to-day lives. Since it’s still so innovative, when AI is introduced, there’s bound to be reluctance. But, to progress, we need acceptability, encouragement and patience; an understanding between AI research and stakeholders that these developments are going to bring huge positive change." Kung: "My main concern is that we need to keep in mind that AI is just a tool to help us. The machine will not replace humans and it cannot tell you what to do. An algorithm can give you a number based on its design. You need to analyze that result and ensure decisions make sense for your business." Peters: "The more we know and learn about AI, the better we can anticipate potential risk areas. These include the ethical aspects of technology, and striving to be consciously unbiased. As we progress, explainability and other model governance practices will help us stay within the right guardrails and mold the necessary regulations." Stoddart: "Lack of diversity concerns me – both in the boardroom and on the programming side. Decisions that we make in our programming are based on assumptions as human beings and our lived experience. If the people writing the code are not diverse, you’re missing out on whole groups of people in the wider society." Q: Can you share with us the “backstory” of how you decided to pursue this career path? He: "My educational background includes cognitive science, neuroscience, and psychology, and it involved a lot of data analysis and modeling. I wanted to understand how humans behave. In my first job, I did essentially the same work — understanding human behavior from large amounts of data — but to detect fraud. That amazed me and driving my focus today." Kazmi: "My education included subjects around analytics, and had a lot of flavor of data science, predictive modeling, mathematics and statistics. AI was very new at the time. I studied these topics and began to understand how data science is developing, and what's the future of it. I really got excited and interested into it. And once I started my career, there was no looking back." Kung: "As a child, I thought I wanted to be an engineer. Statistics was my second choice. But, I am really glad I had the opportunity to follow this path, because statistics and data analysis are amazing. When I started my course, I was so amazed at how data analysis can help you discover a world. You can do anything with data. I realized that this was my true passion." Peters: "I became interested in AI from the business aspects – working in a big data environment, we really needed machine learning and AI to handle data at scale. When joining Experian in the identity and fraud area, our mission was clear – harnessing the power of one of the largest data assets in the world to make a difference; finding new ways to stop fraud." Stoddart: "I studied physics at university and attained a master's in particle physics. But, during my final year, I started to learn about AI and machine learning. It was inspiring, especially how quickly they can have an impact on the world compared to academic research, which can be over many years. Realizing how quickly it was progressing, I thought it would be really exciting to get involved." Q: Can you tell our audience about the most interesting projects you’re working on now? He: "Recently, I’ve been working on use cases and projects surrounding identity. We have been working to link identity data from various sources – online and offline. Here at Experian, we have information from many sources, across different business areas. This project is providing a platform to link all this data together, which in the past was not very easy to accomplish. With this platform to provide linkages, it provides a 360-degree view of a person and helps provide conclusions such as whether two identities are the same person. To do this, we utilize machine learning techniques and AI. It’s very exciting." Kazmi: "I would like to mention something I'm very proud of, which has been a turning point in the way I look at data science solutions. I have the privilege of playing a prominent role in solving for a crucial economic and societal problem of the world, financial inclusion. This issue has historically blocked growth for financially weak and less established sections of society. I am leading data science as part of the initiative, exploring different sources of information beyond credit history, to increase access to financial products. This is the beauty of data science and how it helps us." Kung: "At Experian, I work in a consulting area, so I advise our customers and show them the power of data. Often, it’s not easy for a client to recognize this power. That’s our job – showing them how data can help their business or their decisions. We developed a credit decisioning model for one client using machine learning. This showed them how powerful it can be to use the data we make available to them. They were so amazed with the results. It was a really great experience." Peters: "The newest aspect of my role is leading innovation and strategy for decision analytics in North America. I am constantly on the watch for opportunities to incubate and try to apply Experian’s data and analytics and AI capabilities to solve new problems. We are looking at the role of identity and how we might apply capabilities in new ways. There is an expansion of needs, especially as the world evolves, and how we’re identified is evolving. So the application of Experian’s differentiated capabilities to new areas and markets is an area of focus of mine that I'm really excited about right now." Stoddart: "One of the most interesting projects I've worked on since joining the lab is around fairness of machine learning algorithms, decision-making. It’s about tackling the bias that can come when you use machine learning in a real world scenario. This happens when an algorithm is not being checked properly and it's discriminating against a certain group. To be part of building this vision about treating everybody fairly is great. Especially to be part of a company that values this effort and recognizes that it's going to be increasingly important going forward." Related stories: What is the right approach to AI and analytics for your business? Four fundamental considerations Maximizing impact from AI investment: 4 pillars of holistic AI Forbes: Are we comfortable with machines having the final say? Yi He Yi He works as a data scientist in the Experian NA DataLab. She is dedicated to using machine learning and AI to extract information from large amounts of data to identify, understand and help people, and prevent fraud. She aims to bridge online and offline worlds by linking identity data from these unique sources. With a focus on minimizing friction to customers, Yi’s work helps organizations identify synthetic identities to avoid fraudulent applications. Recently, she contributed to a Covid Outlook & Response Evaluator (CORE) Model – a “heat map” of geographic populations across the U.S. most susceptible to severe cases of Covid-19. Deeba Kazmi In her role as a data scientist at the Experian APAC DataLab, Deeba Kazmi is focused on solving business problems with analytics, including the development of consumer and small to medium enterprise credit risk models that leverage alternative data. Deeba is passionately focused on leveraging AI to create solutions that can help address issues faced by developing markets. Most prominently, this work includes her data science leadership contributions to solving a crucial economic and societal problem – financial inclusion. This effort is helping disadvantaged socio-economic consumer groups gain access to vital credit and financial services by leveraging the power of technology to deliver better outcomes. Jennifer Kung Jennifer Kung is an analytics consultant for Serasa Experian Decision Analytics, where she combines her knowledge of financial services with her data analysis expertise. Jennifer aims to harness the power of data through robust, descriptive and predictive analytical solutions to help clients realize the benefits of the massive amounts of data available to them. She recognizes the magnificence in powering discoveries through data analysis and enjoys revealing these capabilities to businesses who can benefit from these robust, yet approachable solutions. Jennifer enjoys knowing that her work helps to simplify and accelerate decisions that consumers rely on at important times in their life. Kathleen Peters Kathleen Peters leads innovation and business strategy for Decision Analytics in North America. As the prior Head of North America Fraud & Identity business, Kathleen is well-recognized as an identity industry innovator, being named a “Top 100 Influencer in Identity” by One World Identity the last two years. As of 2020, Kathleen was named Chief Innovation Officer for Decision Analytics. Kathleen and her team rely on the power of AI to continuously find new ways to solve customer challenges by defining product strategies, new paths to market and investment priorities. Underlying these efforts is a key focus on the ethical use of technology and the need to be consciously unbiased. Laura Stoddart Laura Stoddart is a physicist turned data scientist who works at the Experian DataLab in London. From her first exposure to AI, she recognized how quickly it can have an impact on the world, which has driven her to get and stay involved in the industry – both professionally and personally. Laura’s recent work has focused on ethical AI, having recently contributed to her first paper addressing the removal of bias from models. In addition, she is concentrated on leveraging emerging datasets to evaluate risk. Outside the DataLab, Laura also volunteers her data science skills to good causes such as Bankuet and helps expose others to the world of AI through mentoring.

In the not so distant past, consumers mostly interacted with their banks in person. Retail customers, for instance, waited in line to make a deposit or talk to a banker. And though the branch may have been busy, a moving line gave comfort to customers that the wait wouldn't be much longer. However, customer expectations in the digital era are dramatically different. According to Experian's new research, one in three customers will abandon a transaction if they have to wait more than 30 seconds, especially when accessing bank accounts. And that's just the tip of the iceberg. When it comes to the digital experience, consumers increasingly want seamless service at every point of their journey. Now, as the Covid-19 crisis continues to accelerate digital demand, financial institutions face more and more customers with similar if not greater expectations. Expectations for things like personalized products, contextual lending decisions, and offline-online seamlessness. And those organizations that understand these evolving needs and deploy cloud-based decision management to ensure they meet them will likely be the winners in this new world. Right here, right now Banking digital transformation was already underway before the pandemic began. Most retail banks provided some customer-facing app. In efforts to automate and streamline business processes, many organizations have also started to migrate their backend infrastructure from on-premise software to the cloud. The pandemic, though, ramped up the demand for everything digital seemingly overnight. Consider that consumer adoption of mobile wallets has jumped 11% since July, largely due to increased contactless in-payments. In the height of the crisis, customers turned to online platforms for financial assistance, from federal loans and grants to mortgage relief and credit applications to small business loans. Businesses that had already migrated to cloud-based solutions were able to scale their response to meet that growth. But that those hadn't? They faced the combined challenge of needing to scale existing services to serve the influx of online customers while simultaneously adding new digital capabilities. As a result, some organizations have ended up playing catch up with their digital offerings. Experian research shows, though, that it's a race worth finishing. Sixty percent of customers say they have higher expectations of their digital experience now than they did before the pandemic. To be sure, the crisis will end. Those expectations, however, are here to stay. A glimpse of the future Banks may see fewer customers in person, but that doesn't mean their service can't be personal. The data analytics features of cloud-based decision management software allow businesses to know more about their customers, providing personalized offers and services right when customers need them most. One bank we work with in India provides an ideal example. They've leveraged deep analytics and decisioning solutions to accelerate their online loan approval process from days down to seconds. They're no longer turning people away who are good candidates for loans. And they've increased their lending without having to take on additional risk. It's a win-win that reveals how organizations can leverage technology to satisfy customer expectations during the height of a crisis and continue to in a post-Covid reality. With cloud-based solutions, organizations can become 100% customer-centric, both in convenience and personalization. The data gives financial institutions a holistic view of their customers, enabling them to anticipate needs and tailor solutions to the individual. Transformation and soon No organization is going to digitally transform overnight. But given the urgency of the demand, there are proven ways to improve their digital customer experience sooner rather than later. Small-to-mid-sized organizations, for instance, should consider out-of-the-box Software-as-a-Service (SaaS) solutions. These offer pre-determined, high-demand use cases such as online eligibility checks and customer acquisition tools. Organizations can modify these solutions to meet specific market needs while saving time on ramping up a fully custom solution. Additionally, even with the imperative to meet the digital demand, it's important to remember that proper planning leads to successful cloud migrations. Consider all the possibilities of what could go wrong and right in terms of incident management, customer service, links to data sources, and more. Rehearse your transition as much as feasible. The preparation may add a bit of time on the front end, but you'll decrease the likelihood of significant disruption when you do migrate and that's worth the effort. The march toward an increasingly digital customer experience only moves in one direction: forward. The pandemic may have pushed financial institutions to speed up their transition to cloud-based decision management, perhaps a bit earlier than some anticipated. But the outcome of a proactive, data-driven organization centered on serving customers promises to be better for everyone. Related stories: New research available: The continued impact of Covid-19 on consumer behaviors and business strategies  Automating fairness: Using analytics to help consumers in a pandemic era In digital transformation, small wins lead to big outcomes 

Several months into the global pandemic and we know that general indicators of risk or stress don’t reveal enough about what’s really going on within your customer portfolios. We also know that most institutions heavily use statistical models in identifying and capturing risk drivers in order to make decisions. Active model calibration in current circumstances can have a measurable effect on approvals and expected loss within a few weeks of being implemented. Banks have managed through economic recessions and other stressed scenarios by adjusting various levers for liquidity and risk. None, however, have ever had to predict consumer behavior in a pandemic. How can credit risk executives regain control over disrupted risk models at a time of constant change? Four key actions to enact now for immediate and sustainable impact:  1. Increase the frequency of model health monitoring Many of the predictive models that financial institutions rely on aren’t stable enough to handle real-world disruptions. Nor are the models re-calibrated frequently enough to appropriately assess risk in the rapidly changing situation we currently face. Monitoring models on a quarterly basis isn’t enough, but that tends to be the average frequency for most financial institutions. Increasing the frequency of model monitoring processes and identifying the need for a change in models sooner leads to significant financial impact. Depending on the asset size of the institution and the specific use case, financial institutions can potentially save millions of dollars in lost revenue or avoided credit losses. Automating the process supports an increased frequency of monitoring while requiring less effort from your analytics team. 2. Carry out ex-ante stress testing for your models Businesses should consider using ex-ante stress testing, in light of the difficulty in maintaining the accuracy of model predictions in changing conditions as well as to meet the heavy governance requirements of new models before their actual use. Traditional ex-post processes are effective in simulating what would have happened historically had a new model been in place. This is an extremely valuable exercise but isn’t very helpful in the current stress environment which is both unique and highly uncertain. Risk managers would like to have a go-forward view on model performance for decisions being made right now, not just a look-back view on decisions made historically. Applying ex-ante stress testing allows us to simulate and analyze a range of possible outcomes based on changing macro conditions, evolving consumer behaviors, and other uncertainties like the quality of underlying data. 3. Make practical, short-term adjustments We’ve seen in previous economic downturns that models can rapidly become unfit for purpose, and the consequences may not be fully apparent until long after the start of the downturn. In such circumstances, you shouldn’t necessarily attempt to make changes that you expect to be robust for many months into the future. There’s a strong case for making adjustments that are designed to address temporary circumstances and reviewing them at an increased frequency. Some businesses are taking a conservative strategy by tightening their credit policies and decisioning strategies. Other businesses are overlaying their models with certain attributes. For example, one could look at the number of open inquiries in the past 30 days. Since we know that attribute is unstable, we can pair it with an attribute that will give you more population stability – such as average open inquiries over the past 6 months. 4. Setup for rapid re-calibration or re-build of models The decision to re-calibrate or re-build a model during the pandemic would depend on multiple factors including the business need and model use case, the performance of the existing model, and the confidence in the quality and relevance of data for the model build. However, it is important that financial institutions and other businesses are set up to rapidly update their models. They should be actively working on re-calibrating/re-building their models in a test environment, evaluate the impact, and be prepared to deploy.  The ability to rapidly update models will be a key differentiator as businesses compete to grow their portfolios and manage losses during and in the aftermath of this pandemic. As with many other aspects of our lives, credit risk management is being challenged by the new reality created by a global pandemic. Whether our response is temporary, or whether the crisis is accelerating an existing trend to be more active in model management, we need to react to maximize our portfolio performance. At the end of the day, none of us have been through a pandemic but we know our models can still work. It’s all about model accuracy and model governance and reducing error rates. By increasing the frequency and efficiency of model monitoring and re-calibration, we can drive business outcomes with more impact than ever before. Learn more: For many organizations, navigating and recovering from these volatile times will remain top priorities as they begin strategizing for the future. Get details on accelerating your digital transformation.

Whether you work for a small or big company, chances are you’ve seen budgets contract in the wake of Covid-19.  There are a lot of factors contributing to it: fluctuating economic outlooks, building up loan loss reserves, and re-directing expenditures to keep employees and customers safe and secure. A recent global study of banks and retailers found that the top area of short-term investment was securing the mobile and digital channels. In fact, it also showed that 80% of businesses put a digital identity strategy in place, a 30-point increase since Covid-19 began and 60% of businesses are planning to increase their budgets for credit risk analytics and fraud prevention, respectively. So why is it that only 32% of banks and retailers feel operationally ready for their customer’s continued demand for digital engagement? The Capex required to invest in new technology these days requires a fiercely competitive business case. Not forgetting to mention, if approved, it could be a while before you see a return on your investment. But it doesn’t mean the latest advancements and innovation available for managing credit risk or fraud risk is out of reach. Getting more out of your existing tools and technologies is easier to implement and quick to deliver results. In fact, since Covid-19 began, hundreds of clients have optimized their use of credit and fraud risk software and analytics, helping them focus on creating more meaningful customer relationships and saving them millions in potential losses. Here are two examples of how you can get the most out of your existing technologies today and a checklist for evaluating your current tools. Device recognition Beyond securing systems against Cybersecurity threats, businesses need to think like the criminals they’re trying to deflect. If it seems like the world all went digital overnight because of Covid-19, then you can bet fraudsters were one step ahead exploiting the blind spots in the customer relationships you quickly moved online. But how do you recognize your customer behind their mobile device or computer screen? One way is to discern a fraudulent (or “mimic”) device from a genuine one. Having access to this information allows you to swiftly see the same device repeating both good and bad behavior and thus have a better chance of isolating the mimic device and mitigating fraud attacks. This is done by creating a strong probabilistic measure to determine whether two events are from the same device or not. How does this help? It helps to reduce over-firing fraud velocity rules and more precisely out-sort fraud events for manual review. It’s not as complicated as it sounds, and many businesses already have access to this device intelligence data which simply requires them to either turn it on or upgrade their fraud management systems to its latest version. In fact, additional device data points are always being added, and upgrading this layer is often recommended as it can provide up to 85% improvement in performance. Bottom-line: Device data bolster the effectiveness of your customer identity and fraud defenses with little impact on operational resources and reduces friction on your customer’s digital experience. Machine learning Innovations in decision management are having an impact on areas traditionally associated with predicting consumer behavior, such as credit risk, collections, and fraud detection. The ubiquity of data nowadays requires the methods used to derive actionable insights to evolve and most lenders globally have started to adopt advanced analytics. Nearly 70% of businesses increasing their use of machine learning for determining creditworthiness since Covid-19 began. For the collections process, it has helped to determine the best way to contact a delinquent customer or the best treatment to use as a customer exits Covid-induced forbearance?  For card, mortgage, and automotive portfolios, machine learning has played a strategic role in creating and implementing pricing strategies to determine the most accurate decisions for financing terms. Perhaps it’s in fraud detection where machine learning is having the biggest impact. Unlike how it’s applied in credit risk decision strategies, machine learning used for fraud detection can be trained to learn and improve with experience without explicitly being told to do so. It excels at solving problems where the “problem space” cannot be defined easily by rules, which makes it a great complement to mature rules-based fraud management systems. Furthermore, machine learning models can take advantage of the different data points from all backing applications at the time of any single transaction, login, or submission. This produces a final decision that’s more accurate than that produced by a simple rules-based approach or manual decision matrix. Attributes that once provided minimal lift when analyzed in a silo may now provide a substantial lift to predict credit risk or prevent a fraud attack when combined with multiple data elements. Conversely, legitimate events that were inadvertently triggered by traditional fraud detection methods can be identified as authentic before having a negative impact on the customer’s experience. Bottom-line: A layered approach continues to be a key component in any credit decision or fraud detection solution and machine-learning models are the final call in your decision workflow strategy so they can leverage all the previous decision data. Checklist: Evaluate whether you’re getting the most from your decision technology Is your current solution providing the results you need? Avoid comfort in patterns and request a business review of your current solution to analyze performance. It may reveal unknown gaps and opportunities to improve your business results. How do your results compare to your peers? Some peer benchmarking is publicly available, but most vendors offer peer (blind) benchmarking using your specific performance data. It’s worth the ask! Are you using all the functionality your tool has to offer? Sometimes decision technology is implemented with a myopic focus on solving a specific problem or used in a specific area despite a broad range of functionality available that covers more use cases. Are you using the most up-to-date version of your tools? Check with your vendor right away and stay informed regarding newer versions. Upgrades generally require less effort and cost than a new solution and by continuously monitoring for the latest version, you’re able to meet current regulatory and policy standards. Are there any ‘add-ons’ available? Your existing decision technology may offer add-ons to enhance your current solution. Add-ons such as new or enriched data sets, updated scores or models or new software features may extend the business usage of a solution to different processes and within additional departments. Are your technologies integrated to enhance your credit risk and fraud risk decision workflow? Integrating your technologies can help you to execute credit and fraud strategies seamlessly with less chance for error, manual intervention, or duplicating actions across disparate systems. Technology is critical in meeting customer demand and staying competitive in any market. It can help balance the demand for internal resources while providing the service your customers deserve. But as organizations look to stay competitive, and agile through a volatile economic time, remember the importance and tangible benefits of optimizing what you already have in place. Related articles: Global research study: The impact of Covid-19 on consumer behaviors and business strategies Podcast: Banking trends and opportunities in the post-Covid-19 era Are traditional online identification methods becoming obsolete? Case study: Layered behavioral biometrics, device intelligence and machine learning 

As businesses continue to figure out the best way to operate through the global pandemic, I’ve been asked by leaders across industries to provide my thoughts and insights around the path forward for businesses, specifically around where to invest and how to manage distributed teams. While my experience drives how I answer these common questions, Experian recently released the results of a global study which helps to demonstrate where businesses are focusing their resources. In a recent global survey among financial services and eCommerce businesses, we found that most companies are focusing on the health and safety of employees and customers, with 42% of those surveyed saying this was their primary focus. Following closely was 32% of businesses who said making operational changes and managing increases in demand across channels and functions is their greatest challenge. That’s a shift from pre-pandemic times when firms were spending more on mobile and digital advancements with intent to strengthen the security of mobile/digital channels, invest advanced analytics (e.g. creation of artificial intelligence models), and improving customer digital account opening and engagement. Top questions I’ve received in the past few months:  Q: As someone with extensive experience managing technology for a distributed team, what advice would you impart to other leaders addressing this for the first time?  A: I don't think there is a single answer, but there are a few things that are mostly common sense. For example, there is a lot of ad-hoc interaction happening in an office. Therefore, consider increasing frequency of any common team and wider meetings, remotely(all-hands, daily stand-ups, staff meetings, or ask-me-anything type of meetings). To compensate for the increase in frequency, consider making these meetings shorter. Another thing is to encourage people to be on video - it adds presence and makes it much easier to collaborate. Also, make sure you have efficient comms-channels (Slack, Teams, Skype, or whatever tool your company uses) which helps with the asynchronous flow and lets everyone jump in. And put the effort in to get good tools. Poor quality connections and audio saps energy and makes it frustrating instead of being useful. It also helps during larger meetings: That way everyone can comment and jump in through a different means, without interrupting. It is also useful to be a bit more disciplined when running meetings. There are many non-verbal cues when we communicate, so to compensate for this a bit more structure (somebody moderating the discussion) may help. Conducting surveys afterward to find out what people find interesting is useful and I also think it is important to talk about the situation, making sure that people can be transparent and recognizing challenges. Finally, in the current situation, where many people have had to adjust their daily lives, we’ve seen a lot of innovation amongst the teams. Anything from virtual coffee breaks outside of regular meetings to virtual curry nights and meet-ups. I think it depends on your team's circumstances but what matters is to stay in close contact. Q: What areas do you believe are most in need of advancement in light of the ongoing global crisis and why? It is hard to predict all of the lasting changes, but I think we will see a continuing acceleration to digital, and some industries that have not had to may now be forced to shift faster — and leaders will need to balance such focus with their priority to best assist employees in a remote environment. According to a recent survey, we know that 50% of consumers anticipate increased spending on items purchased online versus in-person – both in the short-term and within the next 12 months. So, we’ll continue to see people using both remote and digital ways of working, shopping and entertainment, and that will of course continue to drive the need for companies to think about their digital offerings.  And,  by extension how to appropriately secure those transactions for the associated risk and how to make a smooth customer onboarding journey that can be fully digital. I also believe we will see a lot of new and creative use cases from software and analytics, specifically the role of AI. Specifically, we’re seeing rapid changes in behaviors and volumes, and this again emphasizes how important it is to have resilient and scalable systems that can turn around quickly. The current circumstances also highlight the importance and opportunity to take the data we have and apply analytics to drive insight into what impacts we may see and adjust our plans accordingly. This is also an area where businesses are investing. 60% of businesses we surveyed plan to increase their budget for analytics and credit risk management and businesses in the U.K., U.S., Australia, and Spain have already increased adoption of AI and advanced analytics, since Covid-19 began. I’ll continue to monitor these key areas and share significant findings, especially as the pandemic plays out longer than any of us hoped and as businesses start re-opening offices while disparate employees make the best use of resources to support customers. For more about our recent study, check out some highlights here. If you'd like to submit a question to Birger, please email GlobalInsights@experian.com

Download the report People’s changing behaviors to safeguard their health during the ongoing global Coronavirus pandemic has fueled a massive shift to digital channels. As people’s day-to-day routines and behaviors shift, so too is the attention on businesses to find new ways of staying relevant to their customers. Two-thirds of consumers are staying loyal to the businesses they preferred prior to Covid-19. 20% increase in overall online transactions – a 41% increase in online grocery shopping, 40% increase in applying for loans online, and a 22% increase in food delivery or takeout. 50% of consumers surveyed expect to increase their online transactions even more in the next 12-months. Uncertainty for what the next 6-12 months will hold has people and businesses vacillating between optimism and pessimism.  Some likely contributing factors could be public health gains and setbacks for containing the virus, some businesses opening only to close again, and the prospect of some students returning to school in-person and while others go remote – and what all of that means for economic recovery. At the time of our study (June 30 -July 7, 2020), some lenders and retailers are demonstrating more confidence than others, while consumers - many already feeling depleted - are expecting and bracing for an expected second wave of Covid-19.  Consumer financial hardship 65% of people believe their country has not yet recovered from the economic impact of the pandemic. 30% of consumers reported a decline in household income; India saw the largest household decline at 43%. The number of people having difficulty paying their bills has doubled since Covid-19 began. Businesses operational challenges 53% of businesses believe their operational processes have mostly or completely recovered since Covid-19 began. The U.S. (80%) is the most confident and Germany (27%) is the least. Top challenges faced by most businesses globally are the health and safety of their employees and customers, adjusting operations to support customers, and managing increased demand across channels and functions. 1 in 5 businesses surveyed lacks confidence in the effectiveness of their credit risk and collection decisions since Covid-19 began.  Beyond their intense focus on the safety and security of their employees and customers, our research shows that businesses are making strategic investments – to give consumers greater access to goods and services, and to better manage their customer relationships. They’re also exploring automation and cloud technology to relieve operational constraints. Whether it’s a lender providing financial assistance to small businesses and loan re-payment options to customers or it’s a retailer providing essential supplies and services to people who need it most, helping people and delivering on expectations for secure, relevant customer experience is top of mind. Top areas of investment: strengthening the security of mobile and digital channels, new credit risk analytics, and the creation of artificial intelligence (AI) models and increasing digital customer acquisition and engagement. Top 3 solutions businesses believe will improve operational efficiency when supporting customers’ financial needs are automated decision management, cloud-based applications, and artificial intelligence. 60% of businesses plan to increase the budget for analytics and credit risk management. Businesses in the UK, U.S., Australia, and Spain have already increased the adoption of AI and advanced analytics. To solve for the lack of economic precedent, 51% of businesses say they’re asking customers to contribute more information/data and 49% say they’re exploring new or alternative data sources. Download Experian's Decision Analytics Global Insights Report July/August 2020 and learn more about the impact of Covid-19 on consumer behaviors and business strategies