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What were some of the most interesting risk analytics topics last year? Judging from the views on the FICO Blog, risk professionals are keenly interested in new ways to approach risk analytics. Here were the top 5 posts of 2017 in the Risk & Compliance category: US Average FICO Score Hits 700: A Milestone for Consumers Author Ethan Dornhelm explained, “We have pulled the latest FICO Score distribution information based on a snapshot of millions of US consumers’ credit data as of April 2017, and can report that consumer credit health and responsibility continue to be strong! For the first time since we’ve been tracking these stats, the average national FICO Score reached the 700 threshold — some 10 points above what it was just prior to the recession in October 2006.” Read the full post. Millennials and Credit: Are We Missing the Real Story? Andrew Jennings dug into data to explore whether we’re really seeing Millennials turn away from credit. “Compared with 10 years ago, today’s 18-24 year olds have lower credit and store card balances, and while they have more auto loans, the value of these loans did not grow as much as inflation would suggest,” he wrote. “By contrast, growth in student loan debts outpaced inflation, being both greater in number as well as balances; this undoubtedly creates a drag on capacity for other forms of consumer credit.” Read the full post. A New Way to Score Credit Risk – Psychometric Assessments Can you score people who don’t have a credit history using their responses to a questionnaire? Yes, and psychometric risk analytics could expand credit in markets worldwide. Explained author Campbell Scott, “The scoring methodology was developed by EFL Global and marketed by FICO as part of our FICO Financial Inclusion Initiative, designed to open up credit markets around the world to a larger number of unbanked and underserved consumers. The EFL credit risk score is created through a dynamic behavioral design and psychometric assessment that analyzes character traits with a proven relationship to credit risk. It’s an ideal approach for applicants who do not have a credit history and therefore cannot be scored using traditional methods.” He shared the characteristics scored by EFL: Read the full post. 2017 Banking Regulatory Predictions—Brace for a Sea Change Daniel Nestel’s predictions for 2017 involved the Congressional Review Act, cybersecurity legislation, disparate impact and TCPA relief. About the latter, he wrote, “The Federal Communications Commission has not provided much relief for those seeking legal clarity when using modern technology to communicate important non-telemarketing messages to customers’ mobile phones (e.g., fraud alerts) in accordance with the 1991 Telephone Consumer Protection Act (TCPA). Under Chairman Tom Wheeler’s leadership, FCC actions have been widely criticized as further chilling the use of text and automated messaging preferred by many consumers. However, change is on its way with the departure of Wheeler on January 20, 2017, the eventual confirmation of two new FCC commissioners and a much-anticipated decision by the DC Circuit on the industry’s appeal of the FCC’s most recent TCPA guidelines.” Read the full post and see Daniel’s most recent post on this topic, “What’s Happening While We Wait for TCPA Reform?” Using Alternative Data in Credit Risk Modelling This was one of the hot topics from our EMEA risk management forum in 2017, where Manish Gandhi presented an overview of what people are doing with transaction data, telecom data, social profile data, clickstream data, audio / text data and more. He also shared a chart from a project FICO did for a personal loans origination portfolio. “The traditional credit characteristics captured more value than the alternative data characteristics (with the alternative data capturing about 60% of the predictive power), and there was a high degree of overlap between the two. However, by combining the traditional and alternativ

Last year, my regulatory predictions offered little suspense. The winds of change were howling and it was clear that the New Year would be dramatically different than previous ones for those in the U.S. financial services industry. The surprise election of Donald Trump led to a 180-degree course correction of the regulatory agenda that bankers and compliance professionals had grown accustomed to in the wake of the financial crisis. 2017 was highlighted by a series of Executive Orders calling for comprehensive reviews of existing regulations, a series of reports issued by the U.S. Department of the Treasury accompanied by recommendations for significant regulatory reforms, and major changes in leadership (and vision) at many of the federal banking agencies. Also, I wrote about the more than dozen regulations which were rolled back as a result of a rarely triggered statute, the Congressional Review Act. Now, with much of the dramatic shift in the U.S. regulatory landscape complete, we are moving to the next stage, which will focus more on efforts to adopt regulatory reform. Below are a few predictions to watch for in the upcoming twelve months. 1. Bipartisan Dodd Frank Reform Will Become a Reality Bipartisanship on Capitol Hill?  Sounds like a fantasy but look no further than the recent Senate Banking Committee passage of the Dodd-Frank Act regulatory reform bill. While there have been more wide-sweeping proposals introduced in Congress during the past six years, this bill provides a range of changes to the 2010 law that include providing relief to small and mid-size banks. With 10 Republicans and 11 Democrats co-sponsoring the legislation, there is enough support to clear the challenging 60-vote threshold that is needed to get controversial bills through the Senate. Despite a deep partisan divide, look for this bill (perhaps with a few additional tweaks) to make it to the finish line in the first quarter of 2018. 2. A Decision by the DC Circuit Will Spark the Beginning of Much-Need TCPA Changes I admit that I made the same prediction in 2017 and we are still waiting for the United States Court of Appeals for the District of Columbia to issue its decision in the industry’s appeal of the 2015 FCC Telephone Consumer Protection Act (TCPA) Declaratory Ruling and Order.  While a court decision is long overdue, does anyone believe that it won’t be issued in 2018? Once a decision is made the real work will begin. I expect industry to focus on any remaining grey areas that are not resolved by the DC Circuit.  Special attention will be directed to ensuring that unnecessary barriers are removed that prevent customers from receiving important messages via their mobile phones. The new FCC leadership is expected to lead the way in fostering important reforms in this area. It may not be fully completed in 2018 but substantive work will commence during the next 12 months. 3. The Time for Housing Finance Reform Will (Finally) Arrive Policy leaders have been talking about the need for housing finance reform since the government was forced to infuse Fannie Mae and Freddie Mac (GSEs) with $187.5 billion and place the two entities into conservatorship nearly a decade ago. To date, a bipartisan reform proposal has yet to gain any momentum in Congress. So what might change in 2018? A little-known impact of the recently forged tax deal.  Fannie Mae and Freddie Mac hold billions of dollars of “deferred tax assets” on their balance sheets. These assets include items like credits that can used to defray tax bills in future years.  However, the tax reform legislation reduces the corporate tax rate and, in doing so, the value of these deferred tax assets will tumble. As a result, in the beginning of 2018, it is expected that both Fannie and Freddie will have to take another draw from the Treasury. This pending development may have jumpstarted new bipartisan efforts to craft a solution in the Senate, a draft proposal is reported to include a simplified app

“The more things change, the more they stay the same.” That 1849 quip from Jean-Baptiste Alphonse Karr somehow seems fitting for today’s cybersecurity industry. In 2017 we saw massive change in the speed, creativity and magnitude of attacks — yet the industry plodded along on important issues such as timely data breach disclosures. Taking both ends of the spectrum into account, my cybersecurity predictions for 2018 involve cyber scores, 2FA and bio hacks. 1. Your Company’s Cyber Score Will Be as Important as Its Credit Rating In an interview with TechRepublic a few days ago, Gartner research director Jeff Wheatman said, “It's no longer just about understanding whether a company you're going to do business with is credit-worthy, we need to understand what their security posture is, because it's going to have an impact on our security posture.” TechRepublic added: “Historically, organizations would go to credit rating agencies and find out the creditworthiness of their partner, but now that companies are handing out data to their partners, they need to understand what their posture is. As a result of this, we've seen a big uptick in the market for security rating services, he [Wheatman] said.” Exactly! In 2017 FICO has seen a surge in adoption of our Enterprise Security Score, as both cyber insurance providers and their corporate customers recognize the value of an objective measure of cyber security risk. In 2018, I predict that the awareness and usage of enterprise cyber scores will skyrocket. Just as retailers like Costco and Walmart hold their vendors to high standards in logistics and inventory control, in 2018 we will see vendor contracts being terminated, or just allowed to expire, due to the measured level of cyber risk, as encapsulated in independently generated cyber scores. 2. Two-Factor Authentication Will Become Nearly Ubiquitous Although the word “ubiquity” may have gone out of fashion (along with a lot of other buzzwords from the dot-com era), it’s an accurate description of how two-factor authentication will really take off in 2018. 2FA isn’t new, but as a component of the multi-factor authentication required by the PCI standard, it’s set to become much more prevalent before it gets replaced by something newer or better. The reality is, two-factor authentication is extremely effective. With stolen login credentials being a dime a dozen on the dark web, a well-implemented 2FA renders them nearly worthless. Expect to see rapid expansion of 2FA in both consumer applications, as well as fronting access to data and resources in the workplace. 2FA won’t solve all of our security problems, but it’s a relatively inexpensive solution that takes a big bite out of issues related to user authentication. And users don’t yet seem too annoyed by the extra step. 3. We’ll See Our First Biometrics Hack in 2018 It’s true that my predictions blog for 2017 included the ominous statement, “Biometric security data may become the biggest security vulnerability of all.” I still believe that, now even more with the mainstreaming of biometric security measures like the iPhone X’s Face ID facial recognition. The same iPhone X recently unlocked itself for two different women, illustrating the inherent fallibility of biometric authentication techniques. But the confusion of two human beings is not the only problem. The more menacing problem is that as leveraged for security applications, biometrics are nothing more than the stored digital interpretation of a biological feature, which is then associated with your account credentials. Those digital files can be spoofed, stolen or simply rearranged to point to a digital identity other than your own. Sure, it’s difficult. But at one time it was considered difficult to counterfeit a magnetically encoded check or credit card. Biometrics are neither fool-proof nor fraud-proof. And when someone replaces the digital interpretation of your retina with their own, and does a sufficient job covering thei

The election of a new administration along with leadership changes at the Federal Communications Commission (FCC) provided new hope for a broad group of industry and nonprofit organizations who have long been advocating for changes to the Telephone Consumer Protection Act (TCPA). The TCPA reform effort is expected to commence after the United States Court of Appeals for the District of Columbia rules on the challenge (led by ACA International - the Association of Credit and Collection Professionals - and others) to the FCC’s July 2015 Declaratory Ruling and Order. The FCC’s 2015 action is viewed by many as creating additional uncertainty in an area that has witnessed the chilling of the use of modern communications technology.  Nearly 14 months ago, the DC Circuit Court heard oral arguments on this case. I have spoken to legal experts who have reviewed hundreds of DC Circuit cases and they all assert that the timing for a decision in the FCC 2015 Declaratory Ruling appeal is months overdue.  And yet we continue to wait. For those who may need some added context, I detailed some of the challenges with the TCPA and the FCC 2015 Declaratory Ruling in a past blog post. Most recently, in an April 2017 post, I briefly discussed the prospects for TCPA reform. It is expected that once the DC Circuit Court issues its decision it will provide a roadmap for those areas that still need further action by the FCC. So what is happening in the interim while we wait for the court to act? There are some recent TCPA-related developments that merit some attention. TCPA Reform - FCC Explores the Creation of a Reassigned Number Database On July 13, 2017, the FCC issued a Notice of Inquiry (NOI) to explore the creation of a database to share reassigned number information with callers. This has been one of the most problematic TCPA-related issues. Organizations, even after receiving opt-in consent from their customers, face the threat of class action lawsuits when, for example, text messages are sent to a customer whose cellphone number now belongs to another individual.  Reassigned mobile numbers are not isolated incidents.  The NOI indicated that 100,000 numbers are reassigned by wireless carriers every day.  In response to the NOI, industry emphasized that a new database should not take the place of revising current TCPA interpretations. However, should the FCC move forward with a reassigned number database among the key points raised was the importance of having the database serve as a safe harbor for callers and that providers make updates in as real time as possible. The FCC is still in the process of reviewing comments. Another Related Reform Effort - Taking Steps to Address the Problem of Illegal Robocalls Last month, the FCC adopted an Order and Report to authorize voice service providers (e.g., phone companies) to block certain categories of “spoofed” calls that are highly likely to be unlawful.  The action reflects an effort to target illegal robocalls. The Order and Report specifies that phone companies may block spoofed calls when the subscriber of the spoofed number requests it, as well as permit phone companies to block calls from certain categories of spoofed numbers in which the FCC sees no legitimate, lawful purpose for spoofing.  Such calls include invalid numbers, like those with area codes that don’t exist, valid numbers that are not allocated, and valid numbers that are allocated but not assigned.  In a related development, the FCC Order and Report included a Notice of Proposed Rulemaking outlining potential mechanisms to ensure that calls made by banks and other businesses that are erroneously blocked by telephone service providers can be unblocked in a timely manner. All of us dislike unwanted robocalls and these actions appear to be narrowly targeted to address this persistent problem. FICO’s Latest Survey Findings on Customer Communication Preferences FICO recently released findings of a survey of customer sentiments relate

There has been a lot of noise in the fraud marketplace from new market entrants touting the power of machine learning (ML), artificial intelligence (AI) and “new data sources” to fight fraud. Regarding ML and AI, FICO Chief Analytics Officer Scott Zoldi wrote in his post, “Artificial Intelligence: Find It Right in Your Own Backyard”: "The FICO® Falcon Fraud Management solution is the industry leader, protecting two thirds of the world’s payment card transactions against fraud. In far less than the blink of an eye (40-60 milliseconds, to be precise) Falcon scores each authorization that a merchant submits for approval. "Here, Falcon uses adaptive analytics, a type of machine learning in which self-learning models work with the Falcon consortium models to improve the prediction of future fraudulent behavior based on fraud attacks in production. These models score transactions based on recent known fraud and non-fraud transaction data, dramatically improving the models’ sensitivity to changing fraud patterns, and keeping up with the fraudsters." Scott has written extensively about FICO’s use of AI and ML. My post today is about the underlying data we use to build models using machine learning analytics, the FICO Falcon Fraud Consortium. Let’s dive in. The Predictive Power of Consortium Data In thinking about this topic, I am reminded of one of my favorite bon mots from graduate school statistics classes, the sentiments of which have served me well over my career: “In God we trust, all others bring data.” The point here is that data is what teaches you — not your assumptions or biases, but data. And the more data, the better. That was true in the past, and it’s even more true today, as computing power has completely reframed “the art of the possible” when dealing with large volumes of data. In terms of detecting fraudulent card transactions, there’s only one data source that offers the most predictive power, hands down: other card transactions, and lots of them. Specifically, the transaction data from two-thirds of all the cards in the entire world, which pours into the FICO Falcon Fraud Consortium database, every moment of every day. The breadth of this data, and its historic depth, provide a reference data set that is simply unmatched. More than 9,000 institutions and card providers around the world participate in the FICO Falcon Fraud Consortium, contributing data from all of their payment transactions, depersonalized to remove personal and sensitive information. The FICO Falcon Fraud Consortium provides the data foundation of the FICO Falcon Platform, and has continuously improved the accuracy of fraud detection since Falcon launched 25 years ago. What’s more, the consortium provides the additional benefit of alerting all members of fraud threats and patterns seen by an issuer or institution. This “heads up” gives the entire consortium enhanced protection from threats that many member have yet to see. Collaborative Machine Learning The FICO Falcon Fraud Fraud Consortium is the ultimate example of collaborative machine learning. A single data lens into any given issue, including fraud detection, is likely to be myopic; collaboratively solving this problem through data sharing is an inherently stronger approach. Think about your drive home. A million years ago, in the B.I. (Before iPhone) era, you probably took the same route, changing it up if you ran into bad traffic. Now we have technology like Waze, “the world’s largest community based traffic and navigation app” bought by Google in 2013 to enhance its mapping business. Waze users contribute real-time traffic information to the app, giving other users up-to-the-minute insight into traffic patterns. Where I live, in San Diego, Waze can make the difference between a 20-minute cruise and a 90-minute slog during rush hour. And in using Waze, I’ve even stumbled on some neighborhoods I didn’t know existed before and remarked to myself, “So that is how I get up to those houses on

When the sheer computing power of AI meets the transactional complexity of AML, good things happen. I recently had an opportunity to talk about this at the annual Edinburgh Credit Scoring and Control conference. This is a topic I am quite passionate about, as I firmly believe that eliminating the scourge of money laundering could make the world a significantly better place to live. Money laundering is the process of creating the appearance that illicit funds obtained through illegal activity originated from legitimate sources. It is responsible for fueling narcotics, human trafficking and terrorism. According to the United Nations Office on Drugs and Crime, the estimated amount of money laundered globally in one year is as much as 5% of global GDP, or $2 trillion. This is almost the size of the UK’s annual GDP. To tackle this menace, regulators have slapped fines on banks who fail to stop money laundering. The fines have increased 500-fold in a span of a decade and now amount to nearly $10 billion per year. Finding a Needle in a Haystack It’s not that banks haven’t put in controls to tackle this menace. Each bank has dedicated large teams whose sole purpose is to monitor financial and non-financial transactions and identify and create suspicious activity reports, or SARs. They rely on a Know-Your-Customer process, and use gaggles of transaction rules to identify SARs. These processes go through rigorous scrutiny to ascertain that banks are in compliance with the regulators guidance around monitoring practice. But there is a growing awareness that rules alone are often not sufficient to detect money laundering cases, and regulators have started pressuring the banks to adopt sophisticated analytics in their workflows. This is not surprising, as rules-based systems typically generate large amounts of false positives, resulting in thousands of false leads to be investigated. Further, rules reflect past expert knowledge but may not surface sophisticated new money laundering schemes designed to circumvent the rules in place. My talk focused on the use of machine learning for combating money laundering. We have built these analytics using FICO’s battle-proven and patented advanced artificial intelligence systems. We’ve combined our practical, operationalized machine learning expertise — gained over the past 25 years using AI to protect about two-thirds of the world’s payment card transactions — with the power of our FICO TONBELLER Anti-Financial Crime Solutions. This allows us to focus on injecting new machine learning into world-class AML detection methodologies. Soft-Clustering Behavioral Misalignment Score One of the capabilities that I discussed was the soft-clustering behavioral misalignment score. Here’s what that jam-packed phrase means. Instead of using hard segmentation of customers based on the KYC data or sequence of behavior patterns, we use collaborative profiling based on soft clustering. We use collaborative profiling, an unsupervised Bayesian learning technique, to analyze customers’ banking transactions in aggregate and generates “archetypes” of customer behavior. Each customer is then represented as a mixture of these archetypes (rather than as a single archetype) and these archetypes are adjusted in real-time with the financial and non-financial activities of the customer. Customers with similar archetype distribution are clustered together in peer groups. Different clusters have different risk, and customers that are not within any cluster are suspicious. Further, these behavioral soft-clusters allow precision in determining if a customer’s behavior starts deviating from what’s usual for their behavioral peer group. This deviation informs the soft clustering misalignment score, which can be used to prioritize alert-based cases or drive entirely new SAR events undetected by traditional KYC and rule-based systems. Auto-Encoders for Unsupervised Anomaly Scoring We augment the Soft Clustering Misalignment score with

In 1992, Falcon Fraud Manager was introduced: a neural network-based fraud detection system that detects fraudulent payment card transactions in real-time. Since then, despite the proliferation of fraud types, there has been a dramatic reduction in payment card fraud losses across the globe. One of the reasons we developed scoring technology was to help analysts take action on the transactions most likely to be fraudulent. We introduced the Reason Reporter, which, as the name suggests, provide reasons associated with the neural network scores Falcon produces. This capability is, in fact, Explainable Artificial Intelligence (XAI), a topic that recently has become a hot one in light of the European Union’s General Data Protection Regulation (GDPR) and society’s increasing reliance on AI systems. In 1996 we filed a patent for Reason Reporter—indicative of how long, in fact, FICO has been working with Explainable AI. Today, it's still performing very well, and in fact outperforming some newer techniques in the Explainable AI realm. How XAI works in Reason Reporter In the context of Falcon, Reason Reporter is designed to provide explanations for high-scoring cases, where high score is indicative of suspected fraudulent activity on the payment card. During model training, we utilize the reason reporter algorithm to “bin” (sort) each input variable and then compute moments of the historical scores for each bin. In production, when a transaction is scored, the values of the variables are used to assign it to its bin. The smallest deviations of the score from the moments in each bin provide estimates of the input variables responsible for driving the score. Reason codes are then associated to a variable or group of variables. The schematic below of Falcon Case Manager, based on synthetic data used for demo purposes, shows a list of reasons (shown in the yellow box) generated by Reason Reporter for the highlighted transaction.   A Squeeze of LIME As I wrote in my first XAI blog, perturbation-based approaches have recently gained attention due to a technique called Local Interpretable Model-agnostic Explanations (LIME). This technique involves manipulating data variables in infinitesimal ways to see which variables will change the score by the largest amount. LIME then fits a sparse model on the locally dispersed, noise-induced dataset. The indicator variables with the largest coefficients in the model are then reported as the drivers of the score. Working with LIME can be a challenge in that it explains local sensitivity vs. historical data support for reasons. At FICO, as we always do, we compared the newer algorithm to our existing methodologies. For example, we studied a sample of high-scoring fraud cases, which we processed through both Falcon Reason Reporter and LIME. The Falcon neural network was used to generate the scores for the noise-induced perturbation data points required by the LIME methodology. The output of both algorithms was then qualitatively analyzed through expert evaluation. Among the high-scoring cases studied, 11% had exact same top three reason codes generated by Falcon Reason Reporter, and 53% had at two out of three of the same reasons codes. In cases where the two systems differed, the story was interesting. For example, in a high-scoring case where both the algorithms agreed only on one of the three reasons, Falcon Reason Reporter pointed out that the high amount activity and authorization velocity were suspicious. On the other hand, LIME thought that the primary driver of the score was the rate of ATM withdrawal in the last two days, contrary to any evidence of the same. Similarly in another case, with just a few transactions in the history, LIME thought that the reason for the score was PIN decline rate, though there were no PIN declines. We saw recurrence of such behavior again and again. These cases indicate that LIME over-emphasizes the local feature sensitivity induced through its local noise per

To commemorate the silver jubilee of FICO’s use of artificial intelligence and machine learning, we asked FICO employees a question: What does the future of AI look like? The post below is one of the thought-provoking responses, from Sadat Nazrul, an analytic scientist at FICO, working in San Diego. Looking at the next 20 years, I see us moving well beyond the productivity enhancements AI has brought about so far. With the advent of AI, we will be seeing a renaissance in our own personal lives as well as society as a whole. Healthcare Today, our gadgets have the ability to monitor the number of steps we take, the rate of our heart beat, as well as the contents of our sweat. All this rich information allows a team of doctors, engineers and analysts to monitor our well-being and to maintain our peak performance. Similarly, with innovations in genomic sequencing and neural mapping passing FDA trials, we will soon be seeing huge leaps in the field of personalized medicine. AI will help us understand individual physiological needs in order to come up with customized prescriptions and improve our overall health standards. Cognitive Abilities People are keen to improve cognition. Who wouldn’t want to remember names and faces better, to be able more quickly to grasp difficult abstract ideas, and to be able to “see connections” better? Who would seriously object to being able to appreciate music at a deeper level? The value of optimal cognitive functioning is so obvious that to elaborate the point may be unnecessary. Today we express ourselves through art, movies, music, blogs, and a wide range of social media applications. In the field of image recognition, AI can already “see” better than we can by observing far more than the RGB. Virtual Reality allows us to feel as though we have teleported to another world. New idioms of data visualization and Dimensionality Reduction algorithms are always being produced for us to better experience the world around us. We are constantly trying to enhance our 5 senses to go beyond our human limits. 10 years from now, these innovations, coupled with IoT gadgets, will act as extensions of who we are and help us experience our surroundings more profoundly. Emotional Intelligence Just as we enhance our 5 cognitive senses, so too do we enhance our ability to express ourselves and to understand those around us. Many times, we don’t even know what we want. We strive to connect with those around us in a specific way or consume a particular product, just so we could feel a very unique emotion that we fail to describe. We feel much more than just happiness, sadness, anger, anxiety or fear. Our emotions are a complex combinations of all of the above. With the innovations in neural mapping, we will better understand who we are as human beings and better understand the myriad emotional states that we can attain. Our complex emotional modes will be better understood as we perform unsupervised learning on brain waves and help find innovative ways to improve our emotional intelligence. This would include both understanding our own emotions and being more sensitive towards those around us. Perhaps we can unlock new emotions that we have never experienced before. In the right hands, AI can act as our extensions to help us form meaningful bonds with the people we value in our lives. Experience and Imagination The effect of AI on our experience and imagination would result from an aggregate of better cognitive abilities and emotional intelligence. The latest innovations in AI may help us unlock new cognitive experiences and emotional states. Let’s imagine that the modes of experience we have today is represented in space X. 10 years from now, let’s say that the modes of experience are represented in space Y. The space Y will be significantly bigger than space X. This futuristic space of Y may have access to new types of emotions other than our conventional happy, sad and mad. This new space of Y can even allow us to

As a result of several recent policy developments, talk of achieving meaningful US financial regulatory reform is getting louder. The passage in the House of the Republican-backed Financial CHOICE Act of 2017 (Choice Act), the ongoing federal agency activity in response to a number of regulatory reform-related Executive Orders by President Trump, and the positive results from the Fed’s annual supervisory stress tests of large banks are examples of efforts that appear to be moving the reform movement forward. Yet formidable political obstacles remain, leading many to ask: What is achievable and what path will regulatory reform follow? Here is my take. A Legislative Solution Is Not on the Horizon … at Least Not Any Time Soon In June, the House adopted, along a party line vote, the Choice Act, which aims to make broad changes to the 2010 Dodd-Frank statute. While Democrats and Republicans have both discussed their willingness to explore regulatory changes such as targeted relief for small and medium-size banks, the Choice Act has a number of provisions that deeply divide the two parties. These include eliminating the proprietary trading restrictions of the ‘Volcker’ Rule, major changes to the Financial Stability Oversight Council and the diminished authority and independence of the Consumer Financial Protection Bureau (CFPB). Despite the ongoing bipartisan efforts of Senate Banking Committee Chairman Mike Crapo (R-ID) and Ranking Member Sherrod Brown (D-OH), passage of a Senate reform proposal — let alone a compromise bill that could be approved by both chambers — seems elusive. A Crowded Legislative Agenda Perhaps an equally daunting challenge is the already crowded legislative agenda, which includes heavy-lift items like healthcare and tax reform and increased infrastructure spending. There are even rumblings of serious work on housing finance reform. Adding to these legislative initiatives are such must-do items as flood insurance, appropriations funding, the FAA Authorization Act, the National Defense Authorization Act and, of course, the lifting of the debt ceiling. And now there is even more on Congress’s plate. With the recent issuance by the Consumer Financial Protection Bureau (CFPB) of its controversial final arbitration rule, House and Senate Republicans quickly announced their plans to rescind the rule under the Congressional Review Act. Similar efforts, further crowding the legislative calendar, may be pursued if the CFPB moves forward with issuing its final small-dollar lending regulation. In short, it is hard to see how financial regulatory reform gets any congressional consideration in 2017 or in the foreseeable future. So where does that leave us? Is there any hope for financial regulatory reform? Federal Agency Action Will Be Key The promise of realizing any financial regulatory reform likely rests with the federal agencies. The election of a new president has presented President Trump with the opportunity to make sweeping changes within the leadership structure of all the federal agencies, including the independent banking agencies. We have already seen new leaders at the Federal Communication Commission (FCC) and the Securities Exchange Commission (SEC), while the President has recently nominated a new head of the Office of the Comptroller of the Currency (OCC). In the coming months, the President will have the opportunity to nominate new leadership at the Federal Deposit Insurance Corporation (FDIC) and the Federal Reserve, and a year from now at the CFPB. These changes in leadership will assist in the implementation of the President’s financial regulatory reform agenda. On June 12, policy leaders and the public received a preview of the type of regulatory reform initiatives that the administration supports when Treasury Secretary Steven Mnuchin released a 150 page report on recommended changes to the financial system in response to a February Executive Order. In a recent interview, Mnuchin highlighted the l

This is a guest post from Nikola Marcich with the Policy team at the Software & Information Industry Association (SIIA), the principal trade association for the software and digital content industry. Walking into Bernie Madoff’s home in 2005, you would not have found piles of money under a mattress, behind a sofa or in his garage. At the time, Madoff had been running an elaborate Ponzi scheme through the wealth management arm of his business that reached $65 million by the time of his arrest in 2008, deliberately hiding the money intricately within the financial system. Serving as Madoff’s primary bank for over two decades, JP Morgan was one of the culprits of Madoff’s fraudulent actions and money-laundering tactics. In their innocent incompetence to identify clear red flags about Madoff’s returns and file a Suspicious Activity Report (SAR), JP Morgan’s was fined $1.7 billion in 2014. JP Morgan’s fine highlights the broader problem that many global banks had been facing, which was ignoring the warning signings of fraud and money laundering. Increasingly in today’s age, terrorist organizations and dangerous criminals finance their operations by laundering money in global financial institutions, presenting a huge public policy problem for regulators and policymakers. In our artificial intelligence (AI) spotlight this week, we highlight FICO’s AML Threat Score tool, which uses AI to help financial compliance analysts detect money laundering or terrorist financing activities. This tool demonstrates AI’s transformative benefits in anti-money laundering (AML) and fraud detection. In doing so, FICO’s machine learning tool also facilitates stronger criminal justice enforcement and enhances national security by identifying the financing activities of terrorist groups and dangerous criminals. Moreover, the tool not only required human input and knowledge for its development, but also requires human interpretation to determine whether the problem identified by the tool presents a case for money laundering and need for intervention. Innovating AML tools has increasingly become a priority for banks and financial institutions. Regulations to detect and report suspicious activity through SARs have become more strictly enforced. Additionally, with the rise of enormous piles of data, it is very difficult for analysts to sift through the abundance of information. As more cases become flagged for suspicious activity, so too do the number of false positives within the outputted data. Another problem is that, in most instances, money laundering cases deal with multiple interactions or accounts while traditional AML tools flag individual cases, making it incredibly cumbersome for compliance analysts to connect individual interactions or accounts to broader money-laundering threats. As a result, financial institutions have sought to create more productive mechanisms to help compliance employees sift through enormous piles of data and more efficiently report suspicious activity to regulators. Specifically, financial institutions have turned to tools like FICO’s AML Threat Score, which incorporates machine learning to generate its AML tool. Machine learning is an application of AI that gives machines access to data so that the machine, or tool in this case, can learn for itself. As FICO’s Scott Zoldi highlights in a blog about AML and machine learning, “[FICO’s] AML Threat Score prioritizes investigation queues for SARs, leveraging behavioral analytics capabilities from Falcon Fraud Manager. It uses transaction profiling technology, customer behavior sorted lists (BList), and self-calibrating models that adapt to changing dynamics in the banking environment.” With a threat score ranging from 1-999, compliance analysts are able to identify customers whose transactions have a high likelihood of suspicious activity quicker and more accurately. Then, analysts can send in SAR reports to regulators to ensure that they don’t run into the same problems J