What’s Up Next Against Financial Fraud: Current Landscape and Emerging Tools

The prevalence of fraud calls for a new stage of technological tools that can help financial institutions better monitor for and prevent fraud and other forms of financial crime. The Federal Trade Commision received 5.7 million reports of consumer fraud in 2021, totaling $6 billion — an increase of more than 70 percent from 2020. As technology advances, so do the tools and methods developed by hackers and other criminals to steal personal assets and abuse people's private data and. In this article, I will outline how the fraud landscape has evolved, why banks’ current strategies are not sufficient in stopping fraud from taking place, and the role of emerging technology in quashing financial fraud.

The Changing Financial Fraud Landscape

With the increasing onset of digital interactions and records, fraud has ballooned into a $130 billion global financial problem. A wide variety of types of fraud continues to plague the financial industry — such as:

• Identity theft leading to loan fraud, credit fraud, and bank scams
• Advance fee fraud — when a thief tricks a victim into sending money in advance for payments, products, or services that are never fulfilled
• Cashier’s check or fake check fraud
• Fraudulent charities
• Credit card fraud
• Financial account takeovers
• Ponzi schemes and other investment frauds
• Small business fraud (embezzlement, employee theft, etc.)

The most difficult types of fraud to identify, and capture the criminal, are credit card and in particular first party fraud. This financial threat is growing due to the increased online activity that bases itself on people’s sensitive financial data, (e.g., ecommerce, online banking, and digital payment systems). Each day, there is more information logged digitally that hackers and other nefarious actors can break into. Furthermore, hackers are becoming more skilled in escaping detection services from institutions by advancing ways to erase their digital and cybersecurity trails. This is so because the payoff for conducting fraud becomes higher and higher. As a result, more fraudsters spend more time and money to construct more sophisticated fraud schemes.

The Uphill Battle to Find the Right Tool

While banks have been investing a large amount of resources to detect and stop financial fraud, they must first focus on revamping their strategy with the latest technology that can better tackle and stay one step ahead of today’s fraud landscape. Currently, banks have not done enough to shift from traditional rules-based systems in use to stop financial fraud. These rules-based systems are too rigid and are unable to fully analyze all data in order to detect and prevent financial fraud at scale. Banks have veered into additional trending tools, such as machine-learning (ML) based solutions, but these technologies also look at individual features and run a ML model to predict fraudulent patterns. Moreso, low-level banks with smaller budgets are much less effective at preventing and catching fraud. Essentially, traditional processes are reactive and are not equipped to stop fraud from occurring in the first place.

The Need for Novel Technology

With the increased digitization of financial data and banks’ outdated fraud fighting tactics, there is a re-energized role for emerging technologies to help combat financial crime. Organizations must recognize the critical importance of drilling into complex interrelationships among banks, customers, and transactions — and consider technological solutions that can do so, in turn. While machine learning and deep learning continue to boost traditional systems to some extent, graph-enabled technologies are the need of the hour. Graph-based artificial intelligence (AI) platforms are a relatively new tool used in the financial industry that has enhanced skill in analyzing vast data sets to identify fraudulent patterns for transactions. It can process the relationships and provides supporting decision intelligence. In this case, it is the transactions and access requests, between a wide variety of nodes — be it accounts and institutions — in real-time.

One example of graph uses in financial services is to help banking firms that are challenged with efficiently analyzing bank login pages to flag users who are creating fraud accounts. Graph-powered solutions can build graph embeddings to their monitoring system to create features for the model that track when these fraud accounts are created, thereby improving fraud captures while also reducing false positives. As such, graph-based intelligence platforms help banks and partners’ firms overall gain a more proactive edge in stopping and catching financial criminals.

As the economy becomes more and more digitized, a growing amount of data is ripe for the taking by criminals and hackers. It’s true that banks already have some solutions that work to an extent — these include traditional rules-based and ML systems, mainly. However, although they’re sufficient in locating criminals and their tactics, these systems are more reactive and cannot entirely stop fraud from happening in the first place. Graph-fueld AI is an emerging solution in the finance space, among other industries, that is able to analyze data in real-time to stop even modern and sophisticated fraudulent transactions from taking place.

About the Author:
Farshid Sabet, Chief Business Officer of Katana Graph