Tag: Risk Mitigation

With the rise of digital interactions, identity fraud has become an unassuming threat that impacts individuals, businesses, and institutions worldwide. According to the Federal Trade Commission (FTC), 5.4 million consumer reports regarding fraud and consumer protection were filed in 2023. Identity fraud, which is characterized as when an individual's personal information is stolen and used without their consent for fraudulent purposes, has devastating consequences for consumers, including financial losses, damaged credit scores, legal issues, and emotional distress. Financial institutions face damaging consequences beyond financial losses, including reputational damage, operational disruption, and regulatory scrutiny. As technology advances, so do fraudsters' tactics, making it increasingly challenging to detect and prevent identity-related crimes. So, what are financial institutions to do? Industry-leading institutions apply a layered approach to solving fraud that starts with a fraud risk assessment. What is a fraud risk assessment? When opening a new account, banks typically conduct a fraud risk assessment to verify the identity of the individual or entity applying for the account and to assess the likelihood of fraudulent activity. Banks also assess the applicant's credit history, financial background, and transaction patterns to identify red flags or suspicious activity. Advanced fraud detection tools and technologies are employed to monitor account opening activities in real-time and detect signs of fraudulent behavior. This assessment is crucial for ensuring compliance with regulatory requirements, mitigating the risk of financial loss, and safeguarding against identity theft. Understanding the importance of fraud risk assessments A fraud risk assessment is crucial for banks during account opening as it helps verify the identity of applicants and mitigate the risk of fraudulent activity. By assessing the likelihood and potential impact of identity fraud, banks can implement measures to protect customers' assets and protect against losses in their portfolio. Additionally, conducting thorough risk assessments enables banks to comply with regulatory requirements, which mandate the verification of customer identities to prevent money laundering and terrorist financing. By adhering to these regulations and implementing effective fraud detection measures, banks can enhance trust and confidence among customers, regulators, and stakeholders, reinforcing the integrity and stability of the financial system. 10 tools to consider when building an effective fraud risk assessment Several key factors should be carefully considered in an identity fraud risk assessment to ensure thorough evaluation and effective mitigation of identity fraud risks. Financial institutions should consider emerging threats and trends such as synthetic identity fraud, account takeover attacks, and social engineering scams when conducting a risk assessment. By staying abreast of evolving tactics used by fraudsters, organizations can proactively adapt their fraud prevention strategies and controls. Here are 10 tools that can help catch red flags for fraud prevention: Identity verification: Identity verification is the first line of defense against identity theft, account takeover, and other fraudulent activities. By verifying the identities of individuals before granting access to services or accounts, organizations can ensure that only legitimate users are granted access. Effective identity verification methods, such as biometric authentication, document verification, and knowledge-based authentication, help mitigate the risk of unauthorized access and fraudulent transactions. Implementing robust identity verification measures protects organizations from financial losses and reputational damage and enhances trust and confidence among customers and stakeholders. Device intelligence: Device intelligence provides insights into the devices used in online transactions, enabling organizations to identify and mitigate fraudulent activities. Organizations can detect suspicious behavior indicative of fraudulent activity by analyzing device-related data such as IP addresses, geolocation, device fingerprints, and behavioral patterns. Device intelligence allows organizations to differentiate between legitimate users and fraudsters, enabling them to implement appropriate security measures, such as device authentication or transaction monitoring. Phone data: Phone and Mobile Network Operator (MNO) data offers valuable insights into the mobile devices and phone numbers used in transactions. By analyzing MNO data such as subscriber information, call records, and location data, organizations can verify the authenticity of users and detect suspicious activities. MNO data enables organizations to confirm the legitimacy of phone numbers, detect SIM swapping or account takeover attempts, and identify fraudulent transactions. Leveraging MNO data allows organizations to strengthen their fraud prevention measures, enhance customer authentication processes, and effectively mitigate the risk of fraudulent activities in an increasingly mobile-driven environment. Email attributes: Email addresses serve as a primary identifier and communication channel for users in digital transactions. Organizations can authenticate user identities, confirm account ownership, and detect suspicious activities such as phishing attempts or identity theft by verifying email addresses. Analyzing email addresses enables organizations to identify patterns of fraudulent behavior, block unauthorized access attempts, and enhance security measures. Furthermore, email address validation helps prevent fraudulent transactions, safeguard sensitive information, and protect against financial losses and reputational damage. Leveraging email addresses as part of fraud prevention strategies enhances trustworthiness in digital interactions. Address verification: Address verification provides essential information for authenticating user identities and detecting suspicious activities. By verifying addresses, organizations can confirm the legitimacy of user accounts, prevent identity theft, and detect fraudulent transactions. Address validation enables organizations to ensure that the provided address matches the user's identity and reduces the risk of fraudulent activities such as account takeover or shipping fraud. Behavioral analytics: Behavioral analytics enables organizations to detect anomalies and patterns indicative of fraudulent activity. By analyzing user behavior, such as transaction history, navigation patterns, and interaction frequency, organizations can identify deviations from normal behavior and flag suspicious activities for further investigation. Behavioral analytics allows organizations to create profiles of typical user behavior and detect deviations that may signal fraud, such as unusual login times or transaction amounts. Consortia: Consortia facilitate collaboration and information sharing among organizations to combat fraudulent activities collectively. By joining forces through consortia, organizations can leverage shared data, insights, and resources to more effectively identify emerging fraud trends, patterns, and threats. Consortia enables participating organizations to benefit from a broader and more comprehensive view of fraudulent activities, enhancing their ability to detect and prevent fraud. Risk engines: Risk engines enable real-time analysis of transaction data and user behavior to detect and mitigate fraudulent activities. By leveraging advanced algorithms and machine learning techniques, risk engines assess the risk associated with each transaction and user interaction, flagging suspicious activities for further investigation or intervention. Risk engines help organizations identify anomalies, patterns, and trends indicative of fraudulent behavior, allowing for timely detection and prevention of fraud. Additionally, risk engines can adapt and evolve over time to stay ahead of emerging threats, enhancing their effectiveness in mitigating fraud. Orchestration streamlines and coordinates the various components of a fraud detection and prevention strategy. By orchestrating different fraud prevention tools, technologies, and processes, organizations can optimize their efforts to combat fraud effectively. Orchestration allows for seamless integration and automation of workflows, enabling real-time data analysis and rapid response to emerging threats. Step-up authentication: Step-up authentication provides an additional layer of security to verify users' identities during high-risk transactions or suspicious activities. By requiring users to provide additional credentials or undergo further authentication steps, such as biometric verification or one-time passcodes, organizations can mitigate the risk of unauthorized access and fraudulent transactions. Step-up authentication allows organizations to dynamically adjust security measures based on the perceived risk level, ensuring that stronger authentication methods are employed when necessary. By layering these tools effectively businesses remove gaps that fraudsters would typically exploit. Learn more

With great risk comes great reward, as the saying goes. But when it comes to business, there's huge value in reducing and managing that risk as much as possible to maximize benefits — and profits. In today's high-tech strategic landscape, financial institutions and other organizations are increasingly using risk modeling to map out potential scenarios and gain a clearer understanding of where various paths may lead. But what are risk models really, and how can you ensure you're creating and using them correctly in a way that actually helps you optimize decision-making? Here, we explore the details. What is a risk model? A risk model is a representation of a particular situation that's created specifically for the purpose of assessing risk. That risk model is then used to evaluate the potential impacts of different decisions, paths and events. From assigning interest rates and amortization terms to deciding whether to begin operating in a new market, risk models are a safe way to analyze data, test assumptions and visualize potential scenarios. Risk models are particularly valuable in the credit industry. Credit risk models and credit risk analytics allow lenders to evaluate the pluses and minuses of lending to clients in specific ways. They are able to consider the larger economic environment, as well as relevant factors on a micro level. By integrating risk models into their decision-making process, lenders can refine credit offerings to fit the assessed risk of a particular situation. It goes like this: a team of risk management experts builds a model that brings together comprehensive datasets and risk modeling tools that incorporate mathematics, statistics and machine learning. This predictive modeling tool uses advanced algorithmic techniques to analyze data, identify patterns and make forecasts about future outcomes. Think of it as a crystal ball — but with science behind it. Your team can then use this risk model for a wide range of applications: refining marketing targets, reworking product offerings or reshaping business strategies. How can risk models be implemented? Risk models consolidate and utilize a wide variety of data sets, historical benchmarks and qualitative inputs to model risk and allow business leaders to test assumptions and visualize the potential results of various decisions and events. Implementing risk modeling means creating models of systems that allow you to adjust variables to imitate real-world situations and see what the results might be. A mortgage lender, for example, needs to be able to predict the effects of external and internal policies and decisions. By creating a risk model, they can test how scenarios such as falling interest rates, rising unemployment or a shift in loan acceptance rates might affect their business — and make moves to adjust their strategies accordingly. One aspect of risk modeling that can't be underestimated is the importance of good data, both quantitative and qualitative. Efforts to implement or expand risk modeling should begin with refining your data governance strategy. Maximizing the full potential of your data also requires integrating data quality solutions into your operations in order to ensure that the building blocks of your risk model are as accurate and thorough as possible. It's also important to ensure your organization has sufficient model risk governance in place. No model is perfect, and each comes with its own risks. But these risks can be mitigated with the right set of policies and procedures, some of which are part of regulatory compliance. With a comprehensive model risk management strategy, including processes like back testing, benchmarking, sensitivity analysis and stress testing, you can ensure your risk models are working for your organization — not opening you up to more risk. How can risk modeling be used in the credit industry? Risk modeling isn't just for making credit decisions. For instance, you might model the risk of opening or expanding operations in an underserved country or the costs and benefits of existing one that is underperforming. In information technology, a critical branch of virtually every modern organization, risk modeling helps security teams evaluate the risk of malicious attacks. Banking and financial services is one industry for which understanding and planning for risk is key — not only for business reasons but to align with relevant regulations. The mortgage lender mentioned above, for example, might use credit risk models to better predict risk, enhance the customer journey and ensure transparency and compliance. It's important to highlight that risk modeling is a guide, not a prophecy. Datasets can contain flaws or gaps, and human error can happen at any stage.. It's also possible to rely too heavily on historical information — and while they do say that history repeats itself, they don't mean it repeats itself exactly. That's especially true in the presence of novel challenges, like the rise of artificial intelligence. Making the best use of risk modeling tools involves not just optimizing software and data but using expert insight to interpret predictions and recommendations so that decision-making comes from a place of breadth and depth. Why are risk models important for banks and financial institutions? In the world of credit, optimizing risk assessment has clear ramifications when meeting overall business objectives. By using risk modeling to better understand your current and potential clients, you are positioned to offer the right credit products to the right audience and take action to mitigate risk. When it comes to portfolio risk management, having adequate risk models in place is paramount to meet targets. And not only does implementing quality portfolio risk analytics help maximize sales opportunities, but it can also help you identify risk proactively to avoid costly mistakes down the road. Risk mitigation tools are a key component of any risk modeling strategy and can help you maintain compliance, expose potential fraud, maximize the value of your portfolio and create a better overall customer experience. Advanced risk modeling techniques In the realm of risk modeling, the integration of advanced techniques like machine learning (ML) and artificial intelligence (AI) is revolutionizing how financial institutions assess and manage risk. These technologies enhance the predictive power of risk models by allowing for more complex data processing and pattern recognition than traditional statistical methods. Machine learning in risk modeling: ML algorithms can process vast amounts of unstructured data — such as market trends, consumer behavior and economic indicators — to identify patterns that may not be visible to human analysts. For instance, ML can be used to model credit risk by analyzing a borrower’s transaction history, social media activities and other digital footprints to predict their likelihood of default beyond traditional credit scoring methods. Artificial intelligence in decisioning: AI can automate the decisioning process in risk management by providing real-time predictions and risk assessments. AI systems can be trained to make decisions based on historical data and can adjust those decisions as they learn from new data. This capability is particularly useful in credit underwriting where AI algorithms can make rapid decisions based on market conditions. Financial institutions looking to leverage these advanced techniques must invest in robust data infrastructure, skilled personnel who can bridge the gap between data science and financial expertise, and continuous monitoring systems to ensure the models perform as expected while adhering to regulatory standards. Challenges in risk model validation Validating risk models is crucial for ensuring they function appropriately and comply with regulatory standards. Validation involves verifying both the theoretical foundations of a model and its practical implementation. Key challenges in model validation: Model complexity: As risk models become more complex, incorporating elements like ML and AI, they become harder to validate. Complex models can behave in unpredictable ways, making it difficult to understand why they are making certain decisions (the so-called "black box" issue). Data quality and availability: Effective validation requires high-quality, relevant data. Issues with data completeness, accuracy or relevance can lead to incorrect model validations. Regulatory compliance: With regulations continually evolving, keeping risk models compliant can be challenging. Different jurisdictions may have varying requirements, adding to the complexity of validation processes. Best practices: Regular reviews: Continuous monitoring and periodic reviews help ensure that models remain accurate over time and adapt to changing market conditions. Third-party audits: Independent reviews by external experts can provide an unbiased assessment of the risk model’s performance and compliance. These practices help institutions maintain the reliability and integrity of their risk models, ensuring that they continue to function as intended and comply with regulatory requirements. Read more: Blog post: What is model governance? How Experian can help Risk is inherent to business, and there's no avoiding it entirely. But integrating credit risk modeling into your operations can ensure stability and profitability in a rapidly evolving business landscape. Start with Experian's credit modeling services, which use expansive data, analytical expertise and the latest credit risk modeling methodologies to better predict risk and accelerate growth. Learn more *This article includes content created by an AI language model and is intended to provide general information.

It's no secret that the banking industry is essential to a thriving economy. However, the nature of the industry makes it prone to various risks that can have significant consequences. Therefore, effective and efficient risk management is vital for mitigating these risks and enhancing the stability of the banking sector. This is where risk management in banking comes in. Let’s look at the importance of risk management in banking and its role in mitigating risks in the industry. What is risk management in banking? Risk management in banking is an approach used by financial institutions to manage risks associated with banking operations. Establishing a structured risk management process is essential to identifying, evaluating and controlling risks that could affect your operations. The process involves developing and implementing a comprehensive risk management framework consisting of several components, including risk assessment, mitigation, monitoring and reporting. Importance of banking risk management Banks face risks from every angle – changing customer behaviors, fraud, uncertain markets, and regulatory compliance, making banking risk management critical for the stability of financial institutions. There are various risks associated with the industry, including:  Credit risk: The probability of a financial loss resulting from a borrower's failure to repay a loan, which results in an interruption of cash flows and increased costs for collection. How to mitigate: Leverage advanced analytics, data attributes, and predictive models to improve predictability, manage portfolio risk, make better decisionsand acquire the best customers. Market risk:The likelihood of an investment decreasing in value because of market factors (I.e., changes in interest rates, geopolitical events or recessions). How to mitigate: While it is impossible to eliminate market risk, you can diversify your assets, more accurately determine your risk threshold and stay informed on economic and market conditions.  Liquidity risk:The risk that an organization cannot meet its short-term liabilities and financial payment obligations. How to mitigate: More regularly forecast your cash flow and conduct stress tests to determine potential risk scenarios that would cause a loss of liquidity and how much liquidity would be lost in each instance.  Operational risk:Potential sources of losses that result from inadequate or failed internal processes (I.e., poorly trained employees, a technological breakdown, or theft of information). How to mitigate: Hire the right staff and adequately train them, stay up to date with cybersecurity threats and automate processes to reduce human error. Reputational risk: The potential that negative publicity regarding business practices, whether true or not, will cause a decline in the customer base, costly litigation or revenue reductions. How to mitigate: Define your bank’s core ethical values and relay them to stakeholders and employees. You should also develop a reputational management strategy and contingency plan in case a reputation-affecting incident occurs. Risk management in banking best practices Successful banks embrace risks while developing powerful mechanisms to prevent or manage them and stay ahead. By taking a proactive approach and leveraging risk management tools, you can minimize losses, enhance stability and grow responsibly.  The steps for implementing a banking risk management plan, include:  Risk identification and assessment: Financial institutions need to identify potential risks associated with their operations and assess the severity and impact of these risks. Risk mitigation: Once risks have been identified and assessed, financial institutions can implement strategies to mitigate the effects of these risks. There are several strategies for risk mitigation, including risk avoidance, reduction, acceptance and transfer. Risk monitoring and reporting: One of the fundamental principles of a banking risk management strategy is ongoing monitoring and reporting. Financial institutions should continually monitor their operations to identify evolving risks and develop mitigation strategies. Generating reports about the progress of the risk management program gives a dynamic view of the bank’s risk profile and the plan’s effectiveness. Several challenges may arise when implementing a risk management strategy. These include new regulatory rules or amendments, cybersecurity and fraud threats, increased competition in the sector, and inefficient resources and processes. An effective risk management plan serves as a roadmap for improving performance and allows you to better allocate your time and resources toward what matters most.  Benefits of implementing a risk management strategy Banks must prioritize risk management to stay on top of the various critical risks they face every day. There are several benefits of taking a proactive approach to banking risk management, including:Improved efficiency: Enhance efficiency and deploy more reliable operations by identifying areas of weakness or inefficiencies in operational processes.Confident compliance: Ensure you comply with new and amended regulatory requirements and avoid costly fines. Enhanced customer confidence: Foster customer confidence to increase customer retention and mitigate reputational risk. Partnering to reduce risk and maximize growth Effective risk management is crucial for mitigating risks in the banking industry. By implementing a risk management framework, financial institutions can minimize losses, enhance efficiency, ensure compliance and foster confidence in the industry. At Experian, we have a team of experts dedicated to supporting our banking partners. Our team’s expertise paired with our innovative solutions can help you implement a powerful risk management process, as well as: Leverage data to reach company-wide business goals. Lower the cost of funds by attracting and retaining deposits. Protect your business against fraud and risk. Create less friction through automated decisioning. Grow your business portfolio and increase profitability. Learn more about our risk management solutions for banks and fraud risk solutions.

There’s no doubt that fraudulent transactions can end up costing businesses money , which have led many to implement risk-mitigation strategies across every stage of the purchasing journey. However, this very same protection can increase false declines, and the associated friction can create high rates of cart-abandonment and negative impacts for a business’s brand. What is a false decline? A false decline is a legitimate transaction that is not completed due to suspected fraud or the friction that occurs during verification. False declines occur when a good customer is suspected of fraud and then prevented from completing a purchase. This happens when a company’s fraud prevention solution provides inadequate insight into the identity of the customer, flagging them as a potential bad actor. The result is a missed sale for the business and a frustrating transaction and experience for the customer. Are false declines costing your business money? False declines have high revenue and cost consequences for e-commerce marketplace merchants. By denying a legitimate customer purchase at checkout, businesses risk: Loss of new sales directly impacting revenue 16% of all sales are rejected by e-commerce merchants unnecessarily costing businesses ~$11B in sales annually,1 with an estimated 70% of unwarranted friction as a contributing cause. Loss in customer loyalty and lifetime value Blocked payments can leave customers with a poor impression of your business and there’s a good chance they’ll take their business elsewhere. Tarnished business reputation Today’s customers expect businesses and online services to work seamlessly. 81% of consumers say a positive experience makes them think more highly of a brand. Therefore, your brand might take a hit if unnecessary obstacles prevent them from having a good experience. High operational overhead costs The average business manually reviews 16% of transactions for fraud risk. It is estimated that 10 minutes are needed for each review. This inefficiency can be costly as it takes time away from fraud teams who can work on higher priority or strategic initiatives. Businesses can benefit from a seamless and secure payment experience that drives real-time resolution and eliminates a majority of false declines and bottlenecks, ultimately helping increase approval rates without increasing risk. Get started with Experian Link™ - our frictionless credit card owner verification solution. Learn more 1"E-Commerece Fraud Enigma: The Quest to Maximize Revenue While Minimizing Fraud Report" Aite-Novarica Group, July 2022

In 2015, U.S. card issuers raced to start issuing EMV (Europay, Mastercard, and Visa) payment cards to take advantage of the new fraud prevention technology. Counterfeit credit card fraud rose by nearly 40% from 2014 to 2016, (Aite Group, 2017) fueled by bad actors trying to maximize their return on compromised payment card data. Today, we anticipate a similar tsunami of fraud ahead of the Social Security Administration (SSA) rollout of electronic Consent Based Social Security Number Verification (eCBSV). Synthetic identities, defined as fictitious identities existing only on paper, have been a continual challenge for financial institutions. These identities slip past traditional account opening identity checks and can sit silently in portfolios performing exceptionally well, maximizing credit exposure over time. As synthetic identities mature, they may be used to farm new synthetics through authorized user additions, increasing the overall exposure and potential for financial gain. This cycle continues until the bad actor decides to cash out, often aggressively using entire credit lines and overdrawing deposit accounts, before disappearing without a trace. The ongoing challenges faced by financial institutions have been recognized and the SSA has created an electronic Consent Based Social Security Number Verification process to protect vulnerable populations. This process allows financial institutions to verify that the Social Security number (SSN) being used by an applicant or customer matches the name. This emerging capability to verify SSN issuance will drastically improve the ability to detect synthetic identities. In response, it is expected that bad actors who have spent months, if not years, creating and maturing synthetic identities will look to monetize these efforts in the upcoming months, before eCBSV is more widely adopted. Compounding the anticipated synthetic identity fraud spike resulting from eCBSV, financial institutions’ consumer-friendly responses to COVID-19 may prove to be a lucrative incentive for bad actors to cash out on their existing synthetic identities. A combination of expanded allowances for exceeding credit limits, more generous overdraft policies, loosened payment strategies, and relaxed collection efforts provide the opportunity for more financial gain. Deteriorating performance may be disguised by the anticipation of increased credit risk, allowing these accounts to remain undetected on their path to bust out. While responding to consumers’ requests for assistance and implementing new, consumer-friendly policies and practices to aid in impacts from COVID-19, financial institutions should not overlook opportunities to layer in fraud risk detection and mitigation efforts. Practicing synthetic identity detection and risk mitigation begins in account opening. But it doesn’t stop there. A strong synthetic identity protection plan continues throughout the account life cycle. Portfolio management efforts that include synthetic identity risk evaluation at key control points are critical for detecting accounts that are on the verge of going bad. Financial institutions can protect themselves by incorporating a balance of detection efforts with appropriate risk actions and authentication measures. Understanding their portfolio is a critical first step, allowing them to find patterns of identity evolution, usage, and connections to other consumers that can indicate potential risk of fraud. Once risk tiers are established within the portfolio, existing controls can help catch bad accounts and minimize the resulting losses. For example, including scores designed to determine the risk of synthetic identity, and bust out scores, can identify seemingly good customers who are beginning to display risky tendencies or attempting to farm new synthetic identities. While we continue to see financial institutions focus on customer experience, especially in times of uncertainty, it is paramount that these efforts are not undermined by bad actors looking to exploit assistance programs. Layering in contextual risk assessments throughout the lifecycle of financial accounts will allow organizations to continue to provide excellent service to good customers while reducing the increasing risk of synthetic identity fraud loss. Prevent SID

The response to the coronavirus (COVID-19) health crisis requires a brand-new mindset from businesses across the country. As part of our recently launched Q&A perspective series, Jim Bander, Market Lead of Analytics and Optimization and Kathleen Peters, Senior Vice President of Fraud and Identity, provided insight into how businesses can work to mitigate fraud and portfolio risk. Q: How can financial institutions mitigate fraud risk while monitoring portfolios? JB: The most important shift in portfolio monitoring is the view of the customer, because it’s very different during times of crisis than it is during expansionary periods. Financial institutions need to take a holistic view of their customers and use additional credit dimensions to understand consumers’ reactions to stress. While many businesses were preparing for a recession, the economic downturn caused by the coronavirus has already surpassed the stress-testing that most businesses performed. To help mitigate the increased risk, businesses need to understand how their stress testing was performed in the past and run new stress tests to understand how financially sound their institution is. KP: Most businesses—and particularly financial institutions—have suspended or relaxed many of their usual risk mitigation tools and strategies, in an effort to help support customers during this time of uncertainty. Many financial institutions are offering debt and late fee forgiveness, credit extensions, and more to help consumers bridge the financial gaps caused by the economic downturn. Unfortunately, the same actions that help consumers can hamstring fraud prevention efforts because they impact the usual risk indicators. To weather this storm, financial institutions need to pivot from standard risk mitigation strategies to more targeted fraud and identity strategies. Q: How can financial institutions’ exposure to risk be managed? JB: Financial institutions are trying to extend as much credit as is reasonably possible—per government guidelines—but when the first stage of this crisis passes, they need to be prepared to deal with the consequences. Specifically, which borrowers will actually repay their loans. Financial institutions should monitor consumer health and use proactive outreach to offer assistance while keeping a finger on the pulse of their customers’ financial health. For the foreseeable future, the focus will be on extending credit, not collecting on debt, but now is the time to start preparing for the economic aftermath. Consumer health monitoring is key, and it must include a strategy to differentiate credit abusers and other fraudsters from overall good consumers who are just financially stressed. KP: As financial institutions work to get all of their customers set up with online and mobile banking and account access, there’s an influx of new requests that all require consumer authentication, device identification, and sometimes even underwriting. All of this puts pressure on already strained resources which means increased fraud risk. To manage this risk, businesses need to balance customer experience—particularly minimizing friction—with vigilance against fraudsters and reputational risk. It will require a robust and flexible fraud strategy that utilizes automated tools as much as possible to free up personnel to follow up on the riskiest users and transactions.   Experian is closely monitoring the updates around the coronavirus outbreak and its widespread impact on both consumers and businesses. We will continue to share industry-leading insights to help financial institutions manage their portfolios and protect against losses. Learn more About Our Experts: [avatar user="jim.bander" /] Jim Bander, Market Lead, Analytics and Optimization, Experian Decision Analytics, North America Jim joined Experian in April 2018 and is responsible for solutions and value propositions applying analytics for financial institutions and other Experian business-to-business clients throughout North America. He has over 20 years of analytics, software, engineering and risk management experience across a variety of industries and disciplines. Jim has applied decision science to many industries, including banking, transportation and the public sector. [avatar user="kathleen.peters" /] Kathleen Peters, Vice President, Fraud and Identity, Experian Decision Analytics, North America Kathleen joined Experian in 2013 to lead business development and international sales for the recently acquired 41st Parameter business in San Jose, Calif. She went on to lead product management for Experian’s fraud and identity group within the global Decision Analytics organization, launching Experian’s CrossCore® platform in 2016, a groundbreaking and award-winning new offering for the fraud and identity market. The last two years, Kathleen has been named a “Top 100 Influencer in Identity” by One World Identity (OWI), an exclusive list that annually recognizes influencers and leaders from across the globe, showcasing a who’s who of people to know in the identity space.

Although half of businesses globally report an increase in fraud management over the past 12 months, many still experience fraud losses and attacks. To help address these challenges, Experian held its first-ever Fintech Fraud & Identity Meetup on February 5 in San Francisco, Calif. The half-day event was aimed at offering insights on the main business drivers of fraud, market trends, challenges and technology advancements that impact identity management and fraud risk strategy operations. “We understand the digital landscape is changing – inevitably, with technology enhancements come increased fraud risk for businesses operating in the online space,” said Jon Bailey, Experian’s Vice President of Fintech. “Our focus today is on fraud and identity, and providing our fintech customers with the tools and insights needed to grow and thrive.” The meetup was attended by number of large fintech companies with services spanning across a broad spectrum of fintech offerings. To kick off the event, Tony Hadley, Experian’s Senior Vice President of Government & Regulatory Affairs, provided an update on the latest regulatory news and trends impacting data and the fintech space. Next followed a fraud and identity expert panel, which engaged seasoned professionals in an in-depth discussion around two main themes 1) fraud trends and risk mitigation; and 2) customer experience, convenience, and trust. Expert panelists included: David Britton, Experian’s Vice President of Industry Solutions; Travis Jarae, One World Identity’s Founder & CEO; George Kurtyka, Joust’s Co-Founder & COO; and Filip Verley, Airbnb’s Product Manager. “The pace of fraud is so fast, by the time companies implement solutions, the shelf-life may already be old,” Britton said. “That is the crux – how to stay ahead. The goal is to future-proof your fraud strategy and capabilities.” At the close of the expert panel, Kathleen Peters, Experian’s Senior Vice President Head of Fraud and Identity, demoed Experian’s CrossCore™ solution – the first smart, open, plug-and-play platform for fraud and identity services. Peters began by stating, “Fraud is constant. Over 60% of businesses report an increase in fraud-related losses over the past year, with the US leading the greatest level of concern. The best way to mitigate risk is to create a layered approach; that’s why Experian invented CrossCore.” With the sophistication of fraudsters, it’s no surprise that many businesses are not confident with the effectiveness of their fraud strategy. Learn more about how you can stay one step ahead of fraudsters and position yourself for success in the ever-changing fraud landscape; download Experian’s 2019 Global Identity and Fraud Report here. For an inside look at Experian’s Fintech Fraud & Identity Meetup, watch our video below.