Tag: operational efficiency

Generative AI (GenAI) is transforming the financial services industry, driving innovation, efficiency and cost savings across various domains. By integrating GenAI into their operations, financial institutions can better respond to rapidly changing environments. GenAI is reshaping financial services from customer engagement to compliance, leading to streamlined operations and enhanced decision-making. The strategic role of GenAI in financial services Adopting GenAI in financial services is now a strategic imperative. A 2024 McKinsey report (The State of AI in 2024) notes more than a 10% revenue increase for companies using GenAI. As institutions strive to stay competitive, GenAI provides powerful tools to enhance customer experiences, optimize operations, accelerate regulatory compliance, and expedite coding and software development. Key areas where GenAI is making an impact Enhanced customer engagement Financial institutions use GenAI to offer personalized products and services. By analyzing real-time customer data, GenAI enables tailored recommendations, boosting satisfaction and retention. Streamlining and optimizing operations GenAI automates tasks like data entry and transaction monitoring, freeing up resources for strategic activities. This accelerates workflows and reduces errors. Further, GenAI-driven efficiency directly cuts costs. By automating processes and optimizing resources, institutions can lower overhead and invest more in innovation. Deloitte’s Q2 2024 study found AI automation reduced processing times by up to 60% and operational costs by 25%. Accelerating regulatory compliance GenAI simplifies compliance by automating data collection, analysis and reporting. This ensures regulatory adherence while minimizing risks and penalties. According to a 2024 Thomson Reuters survey, AI-driven compliance reduced reporting times by 40% and costs by 15%. Developer coding support for efficiencies GenAI is an invaluable tool for programmers. It aids in code generation, task automation and debugging, boosting development speed and allowing focus on innovation. Gartner’s 2024 research highlights a 30% improvement in coding efficiency and a 25% reduction in development timeframes due to GenAI. Accelerating credit analytics with Experian Assistant Within the credit risk management space, GenAI offers a powerful solution that addresses some known pain points. These relate to mining vast amounts of data for insight generation and coding support for attribute selection and creation, model development, and expedited deployment. Experian Assistant is a game-changer in modernizing analytics workflows across the data science lifecycle. Integrated into the Experian Ascend™ platform, it’s specifically designed for analytics and data science teams to tackle the challenges of data analysis, model deployment and operational efficiency head-on. Capabilities and skills of Experian Assistant Data tutor: Offers comprehensive insights into Experian’s data assets, enabling users to make informed decisions and optimize workflows Analytics expert: Provides tailored recommendations for various use cases, helping users identify the most predictive metrics and enhance model accuracy Code advisor (data prep): Automatically generates code for tasks like data merging and sampling, streamlining the data preparation process Code advisor (analysis): Generates code for risk analytics and modeling tasks, including scorecard development and regulatory analyses Tech specialist: Facilitates model deployment and documentation, minimizing delays and ensuring a seamless transition from development to production Driving more-informed decisions Adopting GenAI will be key to maintaining competitiveness as the financial services industry evolves. With projections showing significant growth in GenAI investments by 2025, the potential for enhanced efficiencies, streamlined operations and cost savings is immense. Experian Assistant is at the forefront of this transformation, addressing the bottlenecks that slow down analytical processes and enabling financial institutions to move faster, more informed and with greater precision. By integrating the capabilities of the Experian Assistant, financial institutions can leverage GenAI in credit risk management, automate data processes, and develop customized analytics for business decision-making. This alignment with GenAI’s broader benefits—like operational streamlining and improved customer experience—ensures better risk identification, workflow optimization, and more informed decisions. To learn more about how Experian Assistant can transform your data analytics capabilities, watch our recent tech showcase and book a demo with your local Experian sales team. Watch tech showcase Learn more

As a community bank or credit union, your goal is to provide personalized care and attention to your customers and members while effectively managing regulatory requirements and operational efficiency. By incorporating tools such as income and employment verification, you can streamline the approval process for both account holders and prospects. With the ability to validate their information in seconds, you'll be able to make well-informed decisions faster and accelerate conversion. In this blog post, we will explore the empowering impact of income and employment verification on financial institutions. Better Data, Better Decisions Choosing a verification partner with an instant employer payroll network allows financial institutions to access reliable and up-to-date income and employment information for confident decision-making. With accurate and timely data at their fingertips, you can gain a deeper understanding of your account holders’ capacity to pay, a critical component to assessing overall financial health. This not only helps mitigate risk but also helps you serve your customers and members more effectively. There are additional benefits to partnering with a verification solution provider that is also a Credit Reporting Agency (CRA) offering FCRA-compliant technologies. These organizations are well versed in compliance matters and can help you more effectively mitigate risk. Streamline Approval Times and Remove Friction When developing your verification process, it is advantageous to adopt a waterfall or multi-step approach that encompasses instant verification, permissioned verification, and, as a last resort, manual verification. This tiered approach will significantly reduce approval times, manage costs effectively, and streamline the approval process. Instant verification relies on advanced technology to provide swift and efficient results. In cases where instant verification is unavailable, the process seamlessly transitions to permissioned verification, where explicit consent is obtained from individuals to access their payroll data directly from their respective providers. Lastly, manual verification involves collecting payroll and employment documents, which is a more time-consuming and costly process. By implementing this comprehensive approach, you can enhance the efficiency and effectiveness of your verification process while maintaining the integrity of the results. A Flexible Solution Community banks and credit unions are integral to the lending industry. It is crucial for them to select a versatile verification solution that can keep pace with the approval speed of both regional and large banks. Given that community banks and credit unions operate in smaller geographic regions compared to larger institutions, it is imperative for them to have a verification solution that is versatile and can be applied across their entire spectrum of loan offerings, including mortgage loans, automotive loans, credit cards, home equity loans, and consumer loans. This adaptability enables community banks and credit unions to consistently serve their account holders and enhances their ability to compete effectively with larger financial institutions. With a robust verification solution in place, community banks and credit unions can confidently navigate the complexities of the lending landscape and deliver exceptional results for their valued account holders. World-Class Service and Support To ensure a seamless verification journey, community banks and credit unions should choose a solution provider that delivers exceptional service and support. From the initial onboarding process and comprehensive training to ongoing troubleshooting and guidance, a dedicated and knowledgeable support team becomes indispensable in establishing a successful verification process. Having hands-on training and support not only instills peace of mind but also empowers community-focused financial institutions to consistently provide a high level of personalized service, fostering trust and loyalty among their customers and members. By investing in a robust support system, community banks and credit unions can confidently navigate the verification landscape and stay ahead in an ever-evolving financial industry, reinforcing their commitment to delivering an outstanding experience to their communities. As a longstanding leader in the financial industry, Experian understands the unique challenges faced by community banks and credit unions. Our verification solution, Experian VerifyTM, provides accurate, efficient, and compliant income and employment verification services. With Experian Verify, community focused financial institutions can navigate the complexities of income and employment verification with ease, achieving new levels of efficiency and success. To learn more about how Experian Verify can benefit your bank or credit union, we invite you to visit our website and schedule a personalized demo. Together, let's unlock the potential of income and employment verification and elevate your financial institution to new heights of success. Learn more

To drive profitable growth and customer retention in today’s highly competitive landscape, businesses must create long-term value for consumers, starting with their initial engagement. A successful onboarding experience would encourage 46% of consumers1 to increase their investments in a product or service. While many organizations have embraced digital transformation to meet evolving consumer demands, a truly exceptional onboarding experience requires a flexible, data-driven solution that ensures each step of customer acquisition in financial services is as quick, seamless, and cohesive as possible. Otherwise, financial institutions may risk losing potential customers to competitors that can offer a better experience. Here are some of the benefits of implementing a flexible, data-driven decisioning platform: Greater efficiency From processing a consumer’s application to verifying their identity, lenders have historically completed these tasks manually, which can add days, if not weeks, to the onboarding process. Not only does this negatively impact the customer experience, but it also takes resources away from other meaningful work. An agile decisioning platform can automate these tedious tasks and accelerate the customer onboarding process, leading to increased efficiency, improved productivity, and lower acquisition costs2. Reduced fraud and risk Onboarding customers quickly is just as important as ensuring fraudsters are stopped early in the process, especially with the rise of cybercrime. However, only 23% of consumers are very confident that companies are taking steps to secure them online. With a layered digital identity verification solution, financial institutions can validate and verify an applicant’s personal information in real time to identify legitimate customers, mitigate fraud, and pursue growth confidently. Increased acceptance rates Today’s consumers demand instant responses and easy experiences when engaging with businesses, and their expectations around onboarding are no different. Traditional processes that take longer and require heavy documentation, greater amounts of information, and continuous back and forth between parties often result in significant customer dropout. In fact, 40% of digital banking consumers3 abandon opening an account online due to lengthy applications. With a flexible solution powered by real-time data and cutting-edge technology, financial institutions can reduce this friction and drive credit decisions faster, leading to more approvals, improved profitability, and higher customer satisfaction. Having a proper customer onboarding strategy in place is crucial to achieving higher acceptance and retention rates. To learn about how Experian can help you optimize your customer acquisition strategy, visit us and be sure to check out our latest infographic. View infographic Visit us 1 The Manifest, Customer Onboarding Strategy: A Guide to Retain Customers, April 2021. 2 Deloitte, Inside magazine issue 16, 2017. 3 The Financial Brand, How Banks Can Increase Their New Loan Business 100%, 2021.

As last year’s high-volume mortgage environment wanes, lenders are shifting focus to address another set of challenges. Continued economic uncertainty lingers as consumers navigate towards recovery. As such, mortgage lenders have less clarity than normal to assess risk and measure performance in their servicing portfolios. On top of that, more lenders are struggling with customer retention than ever before, due to a historically low rate environment in 2020. These combined factors create a new set of challenges servicers will face in the coming months. We explore a few of these challenges below. An incomplete picture of risk The CARES Act accommodation reporting structure has made it challenging for servicing teams to fully understand the impact of forbearance in their portfolios. If looking only at a CARES Act accommodated borrower’s credit profile, there is no indication whether that consumer would otherwise be delinquent or headed towards default. In turn, lenders cannot model out risk based on this information alone. Borrowers’ financial situations can still change rapidly, and some are still struggling to regain their financial footing. Property data also plays a part in a holistic view of risk. Partly due to lack of housing inventory, home equity continues to rise in many areas of the country, yet there is still uncertainty around whether prices are overinflated, whether the market will correct itself and by how much, and the impact the foreclosure moratorium may have on one’s portfolio. And property dynamics continue to change due to consumer migration stemming from the onset of virtual or hybrid work environments, where homeowners are less bound geographically to a place of work. Being able to have insight into a holistic view of risk is critical to navigating the upcoming months in mortgage servicing. Low borrower retention 2020’s prevailing low-rate environment continues to persist well into 2021 creating a big challenge for mortgage servicers in terms of borrower retention. Borrowers continue to be incentivized to refinance, and in some instances multiple times, to capture the savings throughout the life of their mortgage. Every time a borrower refinances, the lender who’s servicing the loan risks losing the borrower to another lender. This portfolio runoff can create losses for the lender; high portfolio run off rates have shown to negatively impact portfolio performance and investor credibility while increasing marketing cost for new customer acquisition. In our Mortgage in 2021 webinar, we point to the sheer magnitude of this – at the end of 2020, a whopping 33% of first mortgages were less than a year old. Additionally, with the uptick in the number of fintech mortgage lenders and aggregation websites, it has become increasingly easy for consumers to shop for alternative options. Being able to predict the consumers likely to refinance can help servicers retain existing customers and reduce losses. Lack of operational efficiency Lenders and servicers had to increase the capacity of their systems, oftentimes at the turn of a dime, due to last year’s record-breaking origination volumes. This led to massive growing pains while simultaneously stress-testing a company’s systems and processes. As a result, the overall cost to produce a mortgage has risen. Borrower data hygiene poses a challenge for many servicers as well. There was a lot of movement in 2020 in terms of mergers and acquisitions which may also affect servicers’ operational efficiency. Marrying several disparate data points during such events can lead to borrower data inconsistencies and duplicates across loan origination systems. And as consumers come out of forbearance or deferral status, servicers are managing more calls to their inbound call centers, increasing the scope of the problem.  Having tools to ensure data accuracy and correct consumer contact information can help reduce operating cost. Conclusion There certainly is a lot of pressure on servicers to optimize and be in a position to efficiently help homeowners in need as forbearance and foreclosure moratoriums end. But with the right data, insights and partners, mortgage servicers can navigate these challenges all while managing risk and enabling the business to grow safely. In our next blog, we highlight what forward-thinking lenders and servicers are focusing on now to navigate the upcoming months in mortgage servicing. Learn more

Lately, I’ve been surprised by the emphasis that some fraud prevention practitioners still place on manual fraud reviews and treatment. With the market’s intense focus on real-time decisions and customer experience, it seems that fraud processing isn’t always keeping up with the trends. I’ve been involved in several lively discussions on this topic. On one side of the argument sit the analytical experts who are incredibly good at distilling mountains of detailed information into the most accurate fraud risk prediction possible. Their work is intended to relieve users from the burden of scrutinizing all of that data. On the other side of the argument sits the human side of the debate. Their position is that only a human being is able to balance the complexity of judging risk with the sensitivity of handling a potential customer. All of this has led me to consider the pros and cons of manual fraud reviews. The Pros of Manual Review When we consider the requirements for review, it certainly seems that there could be a strong case for using a manual process rather than artificial intelligence. Human beings can bring knowledge and experience that is outside of the data that an analytical decision can see. Knowing what type of product or service the customer is asking for and whether or not it’s attractive to criminals leaps to mind. Or perhaps the customer is part of a small community where they’re known to the institution through other types of relationships—like a credit union with a community- or employer-based field of membership. In cases like these, there are valuable insights that come from the reviewer’s knowledge of the world outside of the data that’s available for analytics. The Cons of Manual Review When we look at the cons of manual fraud review, there’s a lot to consider. First, the costs can be high. This goes beyond the dollars paid to people who handle the review to the good customers that are lost because of delays and friction that occurs as part of the review process. In a past webinar, we asked approximately 150 practitioners how often an application flagged for identity discrepancies resulted in that application being abandoned. Half of the audience indicated that more than 50% of those customers were lost. Another 30% didn’t know what the impact was. Those potentially good customers were lost because the manual review process took too long. Additionally, the results are subjective. Two reviewers with different levels of skill and expertise could look at the same information and choose a different course of action or make a different decision. A single reviewer can be inconsistent, too—especially if they’re expected to meet productivity measures. Finally, manual fraud review doesn’t support policy development. In another webinar earlier this year, a fraud prevention practitioner mentioned that her organization’s past reliance on manual review left them unable to review fraud cases and figure out how the criminals were able to succeed. Her organization simply couldn’t recreate the reviewer’s thought process and find the mistake that lead to a fraud loss. To Review or Not to Review? With compelling arguments on both sides, what is the best practice for manually reviewing cases of fraud risk? Hopefully, the following list will help: DO: Get comfortable with what analytics tell you. Analytics divide events into groups that share a measurable level of fraud risk. Use the analytics to define different tiers of risk and assign each tier to a set of next steps. Start simple, breaking the accounts that need scrutiny into high, medium and low risk groups. Perhaps the high risk group includes one instance of fraud out of every five cases. Have a plan for how these will be handled. You might require additional identity documentation that would be hard for a criminal to falsify or some other action. Another group might include one instance in every 20 cases. A less burdensome treatment can be used here – like a one-time-passcode (OTP) sent to a confirmed mobile number. Any cases that remain unverified might then be asked for the same verification you used on the high-risk group. DON’T: Rely on a single analytical score threshold or risk indicator to create one giant pile of work that has to be sorted out manually. This approach usually results in a poor experience for a large number of customers, and a strong possibility that the next steps are not aligned to the level of risk. DO: Reserve manual review for situations where the reviewer can bring some new information or knowledge to the cases they review. DON’T: Use the same underlying data that generated the analytics as the basis of a review. Consider two simplistic cases that use a new address with no past association to the individual. In one case, there are several other people with different surnames that have recently been using the same address. In the other, there are only two, and they share the same surname. In the best possible case, the reviewer recognizes how the other information affects the risk, and they duplicate what the analytics have already done – flagging the first application as suspicious. In other cases, connections will be missed, resulting in a costly mistake. In real situations, automated reviews are able to compare each piece of information to thousands of others, making it more likely that second-guessing the analytics using the same data will be problematic. DO: Focus your most experienced and talented reviewers on creating fraud strategies. The best way to use their time and skill is to create a cycle where risk groups are defined (using analytics), a verification treatment is prescribed and used consistently, and the results are measured. With this approach, the outcome of every case is the result of deliberate action. When fraud occurs, it’s either because the case was miscategorized and received treatment that was too easy to discourage the criminal—or it was categorized correctly and the treatment wasn’t challenging enough. Gaining Value While there is a middle ground where manual review and skill can be a force-multiplier for strong analytics, my sense is that many organizations aren’t getting the best value from their most talented fraud practitioners. To improve this, businesses can start by understanding how analytics can help group customers based on levels of risk—not just one group but a few—where the number of good vs. fraudulent cases are understood. Decide how you want to handle each of those groups and reserve challenging treatments for the riskiest groups while applying easier treatments when the number of good customers per fraud attempt is very high. Set up a consistent waterfall process where customers either successfully verify, cascade to a more challenging treatment, or abandon the process. Focus your manual efforts on monitoring the process you’ve put in place. Start collecting data that shows you how both good and bad cases flow through the process. Know what types of challenges the bad guys are outsmarting so you can route them to challenges that they won’t beat so easily. Most importantly, have a plan and be consistent. Be sure to keep an eye out for a new post where we’ll talk about how this analytical approach can also help you grow your business. Contact us