Tag: fraud analytics

As the sophistication of fraudulent schemes increases, so must the sophistication of your fraud detection analytics. This is especially important in an uncertain economic environment that breeds opportunities for fraud. It's no longer enough to rely on old techniques that worked in the past. Instead, you need to be plugged into machine learning, artificial intelligence (AI) and real-time monitoring to stay ahead of criminal attempts. Your customers have come to expect cutting-edge security, and fraud analytics is the best way to meet — and surpass — those expectations. Leveraging these analytics can help your business better understand fraud techniques, uncover hidden insights and make more strategic decisions. What is fraud analytics? Fraud analytics refers to the idea of preventing fraud through sophisticated data analysis that utilizes tools like machine learning, data mining and predictive AI.1 These services can analyze patterns and monitor for anomalies that signal fraud attempts.2 While at first glance this may sound like a lot of work, it's necessary in today's technologically savvy culture. Fraud attempts are becoming more sophisticated, and your fraud detection services must do the same to keep up. Why is fraud analytics so important? According to the Experian® 2023 US Identity and Fraud Report, fraud is a growing issue that businesses cannot ignore, especially in an environment where economic uncertainty provides a breeding ground for fraudsters. Last year alone, consumers lost $8.8 billion — an increase of 30 percent over the previous year. Understandably, nearly two-thirds of consumers are at least somewhat concerned about online security. Their worries range from authorized push payment scams (such as phishing emails) to online privacy, identity theft and stolen credit cards. Unfortunately, while 75 percent of surveyed businesses feel confident in protecting against fraud, only 45 percent understand how fraud impacts their business. There's a lot of unearned confidence out there that can leave businesses vulnerable to attack, especially with nearly 70 percent of businesses admitting an increase in fraud loss in recent years. The types of fraud that businesses most frequently encounter include: Authorized push payment fraud: Phishing emails and other schemes that persuade consumers to deposit funds into fraudulent accounts. Transactional payment fraud: When fraudulent actors steal credit card or bank account information, for example, to make unauthorized payments. Account takeover: When a fraudster gains access to an account that doesn't belong to them and changes login details to make unauthorized transactions. First-party fraud: When an account holder uses their own account to commit fraud, like misrepresenting their income to get a lower loan rate. Identity theft: Any time a person's private information is used to steal their identity. Synthetic identity theft: When someone combines real and fake personal data to create an identity that's used to commit fraud. How can fraud analytics be used to help your business? More than 85% of consumers expect businesses to respond to their security and fraud concerns. A good portion of them (67 percent) are even ready to share their personal data with trusted sources to help make that happen. This means that investing in risk and fraud analytics is not only vital for keeping your business and customer data secure, but it will score points with your consumers as well. So how can your business utilize fraud analytics? Machine learning is a great place to start. Rather than relying on outdated rules-based analytic models, machine learning can vastly increase your speed in identifying fraud attempts. This means that when a new fraudulent trend emerges, your machine learning software can pinpoint it fast and flag your security team. Machine learning also lets you automatically analyze large data sets across your entire customer portfolio, improving customer experiences and your response time. In general, the best way for your business to use fraud analytics is by utilizing a multi-layered approach, such as the robust fraud management solutions offered by Experian. Instead of a one-size-fits-all solution, Experian lets you customize a framework of physical and digital data security that matches your business needs. This framework includes a cloud-based platform, machine learning for streamlined data analytics, biometrics and other robust identity-authentication tools, real-time alerts and end-to-end integration. How Experian can help Experian's platform of fraud prevention solutions and advanced data analytics allows you to be at the forefront of fraud detection. The platform includes options such as: Account takeover prevention. Account takeovers can go unnoticed without strong fraud detection. Experian's account takeover prevention tools automatically flag and monitor unusual activities, increase efficiency and can be quickly modified to adapt to the latest technologies. Bust-out fraud prevention. Experian utilizes proactive monitoring and early detection via machine learning to prevent bust-out fraud. Access to premium credit data helps enhance detection.  Commercial entity fraud prevention. Experian's Sentinel fraud solutions blend consumer and business datasets to create predictive insights on business legitimacy and credit abuse likelihood. First-party fraud prevention. Experian's first-party fraud prevention tools review millions of transactions to detect patterns, using machine learning to monitor credit data and observations. Global data breach protection. Experian also offers data breach protection services, helping you use turnkey solutions to build a program of customer notifications and identity protection. Identity protection. Experian offers identity protection tools that deliver a consistent brand experience across touchpoints and devices. Risk-based authentication. Minimize risk with Experian's adaptive risk-based authentication tools. These tools use front- and back-end authentication to optimize cost, risk management and customer experience. Synthetic identity fraud protection. Synthetic identity fraud protection guards against the fastest-growing financial crimes. Automated detection rules evaluate behavior and isolate traits to reduce false positives. Third-party fraud prevention. Experian utilizes third-party prevention analytics to identify potential identity theft and keep your customers secure. Your business's fraud analytics system needs to increase in sophistication faster than fraudsters are fine-tuning their own approaches. Experian's robust analytics solutions utilize extensive consumer and commercial data that can be customized to your business's unique security needs. Experian can help secure your business from fraud Experian is committed to helping you optimize your fraud analytics. Find out today how our fraud management solutions can help you. Learn more 1 Pressley, J.P. "Why Banks Are Using Advanced Analytics for Faster Fraud Detection," BizTech, July 25, 2023. https://biztechmagazine.com/article/2023/07/why-banks-are-using-advanced-analytics-faster-fraud-detection 2 Coe, Martin and Melton, Olivia. "Fraud Basics," Fraud Magazine, March/April 2022. https://www.fraud-magazine.com/article.aspx?id=4295017143

Pre COVID-19, operations functions for retailers and financial institutions had not typically consisted of a remote (stay at home) workforce. Some organizations were better prepared than others, but there is a firm belief that retail and banking have changed for good as a result of the pandemic and resulting economic and workforce shifts. Market trends and implications When stay at home orders were issued, non-essential brick and mortar businesses closed unexpectedly. What were retailers to do with no traffic coming through the doors at their physical locations? The impact on big-box retailers like Best Buy, Dick’s Sporting goods, Sears, JCPenney, Nike, Starbucks, Macy’s, Neiman Marcus, Nordstrom, Kohl’s to name a few, has been unprecedented; some have had to shut their doors for good. Over the past several months global retail has seen e-commerce sales grow over 81% compared to the same period last year, according to Card Not Present. Some sectors have seen triple-digit growth year over year. Most online retailers have been ill-prepared to handle this increase in transactional volume in such a short amount of time, which has resulted in rapid fraud loss increases. A recent white paper from Aite Group reported that prior to COVID-19, a large financial institution forecasted an 8% decrease in fraud for 2020, but has since revised the projection to increase 10-15%. What does this all mean?  Bad actors are taking advantage of the pandemic to exploit the online retail channel. The increased remote channel usage—online, mobile, and contact centers in particular—continues to be an area where retailers are exposed. Account takeover, through phishing and relaxed call center controls, is rising as well. Increases in phishing attacks are leading to compromised and stolen identities and synthetic identity fraud. Account takeover (ATO) fraud has increased 347% since 2019 according to PYMNTS.com. A recent survey found more than a quarter of merchants (27%) admit that they don’t have measures to prevent ATO. 24% of merchants can’t identify an ATO during a purchase. 14% of merchants say they are not even aware that an ATO has occurred unless a customer contacts them. When criminals use these compromised accounts to make fraudulent purchases, the merchant loses revenue and the value of the goods. They can also suffer from damage to brand reputation and a loss of customer confidence. A lack of account security can have lasting effects as 65% of customers surveyed say they would likely stop buying from a merchant if their account was compromised, according to that same Card Not Present study. So how can retailers start to identify bad actors with malicious intent? This will be a constant struggle for retailers. Rather than a one size fits all solution, retailers must move toward a strategy that is nimble and dynamic and can address multiple areas of exposure. A fraudster could easily slip by one verification method—for instance with a stolen credential—only to be foiled by a secondary authentication tactic like device identity. A layered fraud strategy continues to be the industry best practice, where both passive and active authentication methods are leveraged to frustrate fraudsters without applying undue friction to “good” consumers. The layered solution should also utilize device risk, identity verification and fraud analytics, with tailoring to each businesses’ needs, risk tolerance, and customer profiles. Learn more about how to build a layered fraud strategy today. Learn more

Experian is recognized as a leading security solution provider for fraud and identity solutions in order to protect customers and financial institutions

We all know that first party fraud is a problem. No one can seem to agree on the definitions of first party fraud and who is on the hook to find it, absorb the losses and mitigate the risk going forward. More often than not, first-party fraud cases and associated losses are simply combined with the relatively big “bucket” of credit losses. More importantly, the means of quickly detecting potential first-party fraud, properly segmenting it (as either true credit risk or malicious behavior) and mitigating losses associated with it usually lies within more general credit policies instead of with unique, targeted strategies designed to combat this type of fraud. In order to create a frame of reference, it’s helpful to have some quick — and yes, arguable — definitions: Synthetic identity: the fabrication of an identity with the intention of perpetrating fraudulent applications for, and access to, credit or other financial services Bust-out: the substantive building of positive credit history, followed by the intentional, high-velocity opening of several new accounts with subsequent line utilization and “never payment” Default payment: intentionally allowing credit lines to default to avoid payments Straight-roller: an account opened with immediate utilization followed by default without any attempt to make a payment Never pay: a form of straight-roller that becomes delinquent within the first few months of opening the account So what’s a risk manager to do? In my opinion, the best methods to consider in the fight against first-party fraud include analytical solutions that take multiple data points into consideration and focus on a risk-based approach. For my money, the four most important are: Models and scores developed with the proper set of identity and credit risk attributes derived from current and historic identity and account usage patterns (in other words, ANALYTICS) — Used at both the account opening and account management phases of the Customer Life Cycle, such analytics can be customized for each addressable market and specific first-party fraud threat The monitoring of individual identity elements at a portfolio level and beyond — This type of monitoring and LINK ANALYSIS allows organizations to detect the creation of synthetic identities Reasonable (e.g., one-to-one) identity and device associations over time versus a cluster of devices or coordinated attacks stemming from a single device — Knowing a customer’s device profile and behavioral usage with DEVICE INTELLIGENCE provides assurance that applications and account access are conducted legitimately Leveraging industry experts who have worked with other institutions to design and implement effective first-party fraud detection and loss-mitigation strategies — This kind of OPERATIONAL CONSULTING can save time and money in the long run and afford an opportunity to avoid mistakes By active use of these methods, you are applying a risk-based approach that will allow you to realize substantial savings in the forms of loss reduction and operational efficiencies associated with non-acquisition of high-risk first-party fraud applications, more effective credit line management of potentially high-risk accounts, better segmentation of treatment strategies and associated spend against high-risk identities, and removal of first-party fraud accounts from traditional collections processes that will prove futile. Download our recent White Paper, Data confidence realized: Leveraging customer intelligence in the age of mass data compromise, to understand how data and technology are needed to strengthen fraud risk strategies through comprehensive customer intelligence.

Exciting research leveraging Experian’s fraud analytics and credit risk modeling are now enabling deposit institutions to understand the impacts of first party fraud and identity theft on their portfolios. Historically, deposit institutions have not considered application fraud to be a major concern and legislation regarding overdraft fees and the opt-in provision for overdraft services will reduce a deposit customer’s ability to spend the bank’s money; however, a determined thief can still: kite checks to commit first party fraud perpetrate an account takeover/identity theft   The result is that deposit institutions will continue to face losses that can be prevented using fraud best practices. The challenge for the institution is knowing whether it is facing first party fraud or identity theft. Increasingly, deposit institutions are turning to Experian to analyze customers that create losses early in the account life cycle in order to make the right modifications to their acquisitions strategies.  Using a combination of fraud analytics built to target specific types of fraud trends, deposit institutions can get a clear picture of the type of behavior that is generating their losses. This type of analysis is quickly climbing the list of fraud best-practices. Armed with the right diagnosis, deposit institutions can respond by prioritizing the right set of fraud alerts.    

Conducting a validation on historical data is a good way to evaluate fraud models; however, fraud best practices dictate that a proper validation uses properly defined fraud tags. Before you can determine if a fraud model or fraud analytics tool would have helped minimize fraud losses, you need to know what you are looking for in this category.  Many organizations have difficulty differentiating credit losses from fraud losses.  Usually, fraud losses end up lumped-in with credit losses. When this happens, the analysis either has too few “known frauds” to create a business case for change, or the analysis includes a large target population of credit losses that result in poor results. By planning carefully, you can avoid this pitfall and ensure that your validation gives you the best chance to improve your business and minimize fraud losses. As a fraud best practice for validations, consider using a target population that errs on the side of including credit losses; however, be sure to include additional variables in your sample that will allow you and your fraud analytics provider to apply various segmentations to the results.  Suggested elements to include in your sample are; delinquency status, first delinquency date, date of last valid payment, date of last bad  payment and indicator of whether the account was reviewed for fraud prior to booking. Starting with a larger population, and giving yourself the flexibility to narrow the target later will help you see the full value of the solutions you evaluate and reduce the likelihood of having to do an analysis over again.  

In a previous blog, we shared ideas for expanding the “gain” to create a successful ROI to adopt new fraud best practices  to improve.  In this post, we’ll look more closely at the “cost” side of the ROI equation. The cost of the investment- The costs of fraud analytics and tools that support fraud best practices go beyond the fees charged by the solution provider.  While the marketplace is aware of these costs, they often aren’t considered by the solution providers.  Achieving consensus on an ROI to move forward with new technology requires both parties to account for these costs.  A more robust ROI should these areas: • Labor costs- If a tool increases fraud referral rates, those costs must be taken into account. • Integration costs- Many organizations have strict requirements for recovering integration costs.  This can place an additional burden on a successful ROI. • Contractual obligations- As customers look to reduce the cost of other tools, they must be mindful of any obligations to use those tools. • Opportunity costs- Organizations do need to account for the potential impact of their fraud best practices on good customers.  Barring a true champion/challenger evaluation, a good way to do this is to remain as neutral as possible with respect to the total number of fraud alerts that are generated using new fraud tools compared to the legacy process As you can see, the challenge of creating a compelling ROI can be much more complicated than the basic equation suggests.  It is critical in many industries to begin exploring ways to augment the ROI equation.  This will ensure that our industries evolve and thrive without becoming complacent or unable to stay on top of dynamic fraud trends.  

By: Heather Grover In past client and industry talks, I’ve discussed the increasing importance of retail branches to the growth strategy of the bank. Branches are the most utilized channel of the bank and they tend to be the primary tool for relationship expansion. Given the face-to-face nature, the branch historically has been viewed to be a relatively low-risk channel needing little (if any) identity verification – there are less uses of robust risk-based authentication or out of wallet questions. However, a now well-established fraud best practice is the process of doing proper identity verification and fraud prevention at the point of DDA account opening. In the current environment of declining credit application volumes and approval across the enterprise, there is an increased focus on organic growth through deposits.  Doing proper vetting during DDA account openings helps bring your retail process closer in line with the rest of your organization’s identity theft prevention program. It also provides assurance and confidence that the customer can now be cross-sold and up-sold to other products. A key industry challenge is that many of the current tools used in DDA are less mature than in other areas of the organization. We see few clients in retail that are using advanced fraud analytics or fraud models to minimize fraud – and even fewer clients are using them to automate manual processes - even though more than 90 percent of DDA accounts are opened manually. A relatively simple way to improve your branch operations is to streamline your existing ID verification and fraud prevention tool set: 1. Are you using separate tools to verify identity and minimize fraud? Many providers offer solutions that can do both, which can help minimize the number of steps required to process a new account; 2. Is the solution realtime? To the extent that you can provide your new account holders with an immediate and final decision, the less time and effort you’ll spend after they leave the branch finalizing the decision; 3. Does the solution provide detail data for manual review? This can help save valuable analyst time and provider costs by limiting the need to do additional searches. In my next post, we’ll discuss how fraud prevention in DDA impacts the customer experience.

In my previous two blog postings, I’ve tried to briefly articulate some key elements of and value propositions associated with risk-based authentication.  In this entry, I’d like to suggest some best-practices to consider as you incorporate and maintain a risk-based authentication program. 1. Analytics – since an authentication score is likely the primary decisioning element in any risk-based authentication strategy, it is critical that a best-in-class scoring model is chosen and validated to establish performance expectations.  This initial analysis will allow for decisioning thresholds to be established.  This will also allow accept and referral volumes to be planned for operationally.  Further more, it will permit benchmarks to be established which follow on performance monitoring that can be compared. 2. Targeted decisioning strategies – applying unique and tailored decisioning strategies (incorporating scores and other high-risk or positive authentication results) to various access channels to your business just simply makes sense.  Each access channel (call center, Web, face-to-face, etc.) comes with unique risks, available data, and varied opportunity to apply an authentication strategy that balances these areas; risk management, operational effectiveness, efficiency and cost, improved collections and customer experience.  Champion/challenger strategies may also be a great way to test newly devised strategies within a single channel without taking risk to an entire addressable market and your business as a whole. 3. Performance Monitoring – it is critical that key metrics are established early in the risk-based authentication implementation process.  Key metrics may include, but should not be limited to these areas: • actual vs. expected score distributions; • actual vs. expected characteristic distributions; • actual vs. expected question performance; • volumes, exclusions; • repeats and mean scores; • actual vs. expected pass rates; • accept vs. referral score distribution; • trends in decision code distributions; and • trends in decision matrix distributions. Performance monitoring provides an opportunity to manage referral volumes, decision threshold changes, strategy configuration changes, auto-decisioning criteria and pricing for risk based authentication. 4. Reporting – it likely goes without saying, but in order to apply the three best practices above, accurate, timely, and detailed reporting must be established around your authentication tools and results.  Regardless of frequency, you should work with internal resources and your third-party service provider(s) early in your implementation process to ensure relevant reports are established and delivered. In my next posting, I will be discussing some thoughts about the future state of risk based authentication.