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As we enter the beginning of the end of this global crisis, the role of data, analytics, and credit risk decisioning takes on even greater significance than before. Consumers face uneven roads to recovery, with some ready to spend again and others still mired in pandemic-related financial stress. And businesses of all sizes report their operations are recovering but there’s still a way to go. A key difference we saw is that companies that adapted to serve customer needs digitally are faring much better. Our 2021 Global Decisioning eBook, Navigating a new era of credit risk decisioning, looks at how consumers are stabilizing their finances and how businesses are returning to growth. A recent survey among 9,000 consumers and 2,700 businesses across ten countries worldwide reveals the importance of lenders prioritizing digital transformation, and the role of advanced data and analytics in enhancing the customer experience. The pandemic fall-out is impacting everyone differently: 1 in 3 consumers remains concerned about their finances – paying bills and managing credit Whereas high-income households are no longer reducing their discretionary spending Navigating this varied credit landscape requires a deep understanding of customer needs on both ends of the spectrum. However, business confidence in the consumer credit risk management analytics models dropped over the past year from 71 percent to 61 percent. Smaller lenders with revenues ranging from $10M to $49M have seen the sharpest decline from 72 percent to 57 percent in the past six months. Adapting data and analytics to a rapidly changing customer base: Almost 50% of businesses surveyed said their dedicate more resources to enhance analytics One-third of businesses are planning to re-build their models from scratch Recalibrating credit models is one thing, but lenders also need to rethink their data sources to better understand current customer profiles. The data inputs generated by the pandemic have impacted credit risk models and machine learning applications in unexpected ways. For example, widespread payment holidays and government stimulus programs may be masking customers’ true financial circumstances. According to Recovery Insights, a separate study published by Experian North America: Delinquency prior to the pandemic is a strong indicator of future risk. Accounts exiting an accommodation period are 2x more likely to become delinquent than are accounts that never received an accommodation. Payment on debt during accommodation indicated a reduced risk for subsequent delinquency. Amidst the pandemic lockdown, consumers turned online to manage finances and connect with lenders – including older consumers.  And while the pandemic pushed consumers online out of necessity, now that they’re there – it’s become a preference – as overall digital gains are holding above pre-pandemic levels. Lenders have a new digital imperative to meet consumers’ evolving needs for continued digital engagement. Consumer expectations of digital experiences 55% of consumers have higher expectations of their digital experience since Covid-19 began 43% of consumers surveyed age 70+ reported digital banking throughout the pandemic 14% of consumers surveyed age 60-69 applied for a new loan or card online The importance of a digital-first approach has revealed itself and many companies have put a digital customer journey in place since Covid-19 began. The future, however, is more than providing online services. It’s about knowing your customers well enough to anticipate their credit needs and using tools to automate the process and reduce risk. Adapt or lose customers 9 in 10 businesses have a digital customer journey in place 1 in 4 consumers have taken their business elsewhere because a company didn’t adapt to their digital needs Online customer experience and credit risk management are more connected than ever before. And, businesses need technology that supports the entire customer journey, from onboarding to customer management to collections. Five digital investments businesses are prioritizing the new era of credit risk management: Implement new machine learning models for customer decisions Increase digital acquisitions and engagement Understand their customer base (affordability, value, behavior) Automate customer decisions Increase value of existing customers Access the report here to get more consumer trends and find out what the future of decisioning means for businesses looking to return to growth. Stay in the know with our latest insights:

The 2021 Global Identity and Fraud Report highlights consumer trends, with a deep connection between recognition, fraud prevention, and customer experience.

Did you miss these March business headlines? We’ve compiled the top global news stories that you need to stay in-the-know on the latest hot topics and insights from our experts. How to build consumer trust without sacrificing the experience This Forbes article explores the relationship between brand trust and customer experience using the lastest research from Experian. Read about how simple verification methods, personalization and ensuring the use of robust ethical data practices, can be key to driving consumer trust. Using AI for digital identification, fraud prevention, and increased ROI In this IOT for all article, Eric Haller, Vice President & General Manager of Identity, Fraud & DataLabs, covers how investing in AI will not only improve customer experience but also impact a company's bottom line. Digital and online trends in a post-pandemic world Nasdaq #TradeTalks Video Broadcast Series featuring Steve Wagner, Global Managing Director of Decision Analytics, covers what we should expect to see from consumers and businesses in a 2021 post-pandemic world when it comes to digital and online. How current data-driven digital identity impacts businesses and consumers 60 percent of consumers are using a universal mobile wallet to make digital payments according to Experian's latest research. Eric Haller, Vice President & General Manager of Identity, Fraud & DataLabs, talks to CNP about how digital identity solutions that use AI and ML will reduce fraud and improve customer experience when it comes to digital transactions. The sophisticated customer and the need for adaption This Q&A in the Digital Journal with Donna DePasquale, Vice President & General Manager of Global Decisioning, looks at how businesses should respond to consumers seeking a better online experience. Stay in the know with our latest insights:

AI excellence drives intelligent agent-customer assist for complex, regulated, and subjective interactions, revolutionizing digital customer interactions.

Understand the latest advanced technologies available to propel your business forward — including award-winning, AI-powered consumer lending

Artificial Intelligence (AI) offers people and companies many advantages, and we interact with it every day. From the technology we use to do simple things like heating and cooling our homes, to more advanced tools that map potential disease outbreaks across the globe. AI is also being used more and more in the financial services sector – from matching new customers with the right loan and terms to assisting with transactions in real-time online. In a recent study, we found two-thirds of businesses surveyed globally are using AI to help manage their businesses today. More businesses are keen to use AI but are challenged to fulfill requirements for decision explainability – a must-do for ensuring consumers are treated fairly. The history of AI AI stems from the realization of the potential of computation. The father of theoretical computer science and AI, Alan Turing, introduced a theoretical mathematical model of computation – aptly named the Turing Machine – in 1936. He described this machine as being capable of computing anything computable. By 1950, his work posed the question “Can machines think?” He introduced the Turing Test, still in use today to subjectively evaluate whether a machine is intelligent based on its ability to have a conversation. Six years later, in 1956, prominent computer scientists proposed the famous Dartmouth Summer Project. Advanced concepts were introduced and discussed and the term "artificial intelligence" was first coined. Over the following two decades, AI flourished. Computers became not only faster, cheaper, and more accessible, but they were progressively able to store more information. Meanwhile, machine learning algorithms continued to improve, getting the interest of experts in different fields and industries and taking the realm of artificial intelligence to a tipping point in the early ’80s. Back then, John Hopfield and David Rumelhart popularized “deep learning” techniques which allowed computers to learn from experience. Meanwhile, Edward Feigenbaum introduced expert systems which mimicked the decision-making process of a human expert, allowing the program to ask an expert in a field how to respond in a given situation and to learn from it. How can AI benefit both businesses and consumers? Following these early milestones, the advanced analytics sector has experienced explosive growth – with AI impacting many aspects of our lives today. While most people have come to realize that AI can be beneficial, even since the early days, there have been many different views on how those involved in programming the algorithms must take the necessary steps to prevent AI from reinforcing stereotypes, widening wealth and educational gaps, or providing incorrect answers at critical junctures such as in a medical setting. As an example of what not to do: a famous language model was trained using 8 million pages sourced directly from the web. So, implicit in this model are the preconceptions and biases included in its training data. In this case, it led to a model with a trend towards greater male bias in more senior, higher-paying jobs. How to determine fairness in AI models So how can we ensure that the use of AI does not reinforce societal racism, sexism, or other stereotypes? That leads us to define fairness. It’s the impartial and just treatment of people without favoritism or discrimination; when no unjustified distinctions occur based on groups, classes, or other categories to which they are perceived to belong. But, within the world of AI, there are varying approaches to fairness associated with different metrics to evaluate and adopt this sought-after algorithmic fairness. Any solution requires defining dimensions of fairness, but realistically, it’s extremely hard to capture all these very sensitive variables and risky to store and process them. To truly determine if an AI system is fair requires an enormous amount of data and expertise. Additionally, promoting fairness requires an approach across the entire data science life cycle and modeling life cycle. All areas must be considered from the approach to data collection to ongoing evaluation of decisions. And, while fairness in AI is not ‘once and done’ or easily solved, the good news is that it is an area of great focus for regulators, academics, and data and analytics industry experts, like our peers at Experian. The growing importance of transparency and explainability Models generally compute calculations that are complex and involve more dimensions than we can directly comprehend. Given this processing step from model-input-to-model-output is unclear, it leads to questions around how a model has come to a decision. Importantly, how can one be sure that the model is behaving as expected? There are different ways to address explainability. One includes an understanding of how different inputs of a model affect its outputs. Shapley values, introduced by Nobel prize winner Lloyd Shapely, consider an aggregate of marginal contributions for all possible combinations. Another technique involves explaining the behavior of a decision by identifying model constants verse variables to extract what drove a decision and how. Yet another method uses counterfactual explanations, identifying the precise boundary where a decision changes. This method is easy to communicate since it involves statements such as if X had not occurred, Y would not have happened. As in the case of fairness, there’s an on-going dialogue around explainability, underpinned by current and yet to emerge new techniques that maintain model accuracy and improve explainability. Artificial intelligence is past its infancy stage. It’s already had an impact on our daily lives and is becoming increasingly ubiquitous. Fairness, along with a transparent and explainable approach are key ingredients to help this field continue its transition to maturity.

We explore big data, advanced analytics, and decision intelligence trends and what's behind them: the cloud, machine learning, and real-time streaming.

Top December headlines include demand and expectations for digital, women leading AI and protecting against fraudsters over the holiday shopping season.

Of businesses surveyed, nearly 70% have used machine learning or AI in business management; 60% will increase budget for analytics/customer creditworthiness

Customer loyalty relies on businesses transforming to keep pace with consumer demand for safe and convenient digital experiences.

The artificial intelligence debate is still ongoing for some people and businesses, but these women in AI are helping ensure it brings the world real value

With another resurgence of the coronavirus, the impact of Covid-19 will again be felt by businesses—but this time consumer expectations are much higher