AI in FinTech: Fraud and Risk Controls

Artificial intelligence is an emerging technology being applied over multiple industries and sectors over the last few years. As we move into a new decade, AI is showing tremendous synergy in financial services globally as a boost to the enhancements offered by financial technology (FinTech).

Disruption within banking after the Financial Crisis of 2008 - 2009 showed how much innovative technology could improve operations within financial institutions and credit unions. The early FinTech startups of 2010 - 2015 proved that beneficial changes can also made for consumers and businesses when it comes to their banking experience. Artificial intelligence has turbocharged improvements for both customers and organizations in applying FinTech.

Applications of AI in FinTech cover fraud and risk management, customer experience and support, and asset/wealth management. In this first section of a 3-part series on AI in FinTech, we explore how artificial intelligence is helping banks and startups deter fraud from users and mitigate financial risk on their platforms. Let’s first briefly cover what AI is.

What is Artificial Intelligence in fintech?

At a general level, AI is subset within computer science that enables computers to perform functions originally meant for individuals. The component of intelligence comes from training computer models based on large volumes of actual data, to make a prediction or decision towards a task.

The development of AI lands in two areas: building systems to think like humans (by providing info or recommendations), or creating models to complete basic tasks. As the computing power and bandwidth to develop AI become widely accessible, the applications towards financial services has quickly increased. Reducing manual tasks and back-office processes are early wins for AI.

Artificial intelligence in FinTech can be seen as partnership that takes the industry to the next level. With so much consumer and business transaction data flowing throughout financial institutions, card networks, and payment organizations — the ability to drive insights for improvements at every level is huge. Removing human transaction errors, having a deeper understanding of customers, and digitally transforming the future of finance becomes possible. The largest banks in 2019 were estimated to invest over $5B in AI applications with projection of creating over $250B in value from profits.

AI APPLICATIONS of FRAUD AND RISK MANAGEMENT

Fraud Detection and Compliance

There is an estimated total of over $70B spent annually in compliance by US banks. Fraud cases regarding payments is also rising year-over-year. AI is the leading preventive measure against financial fraud, utilized to review enormous amounts of data to identify patterns of risk. Suspicious activities and individuals can then be separated for further review to isolate actual fraud and false positives.

Artificial intelligence is being actively used to reduce the latest financial crime in bonuses from signing up for new deposit accounts or credit cards, and online purchases of monthly subscriptions. Some tech giants (such as Alibaba) have their own fraud detection program through a customer portal, such as chatbot.

Preventing Account Takeovers

Due to the availability of personal data and recurring security breaches, private consumer identities are being stolen and altered to commit financial fraud through account takeovers. There are over $4B in annual losses due to ATOs, most taking place in online shopping. Identity theft and fake personal information creates profiles for cybercriminals to make purchases undetected. ATO tactics involve social engineering, smartphone hijacking, and password spraying.

Artificial intelligence collects vast volumes of data sets to help flag fraudulent users by location, IP address, email, browser, phone number, and operating system. Identifying threats quickly reduces the amount of fraud and prevents tampered credentials or profiles from being used again.

Fighting Against Money Laundering

Financial services companies and institutions are constantly on defense from potential money laundering and terrorist financing structures, which pose the biggest challenge globally. These schemes usually involve various small deposits as currency or financial instruments, and then large disbursements to international banks or tax-haven jurisdictions. Over the last two decades, anti-money laundering (AML) efforts have required banks and government organizations to become rigorous in identifying and preventing patterns.

Due to lack of national or global datasets of activity, having a unified approach in making consistent predictions of crimes has been difficult. AI models, specifically artificial neural networks, have shown improvement over standard statistic models in identifying suspicious patterns. Analytics from big data, from companies such as ThetaRay, provides insights in comparing current and historical behavior. Outlier transactions, such as influx of cash deposits or multiple international wire transfers, are easily captured and reported.

Bank Surveillance

Despite the annual decrease in customer usage of local branches of banks and credit unions, the rate of robbery attempts is about 3K each year (from a 2018 FBI report). Enhancements to bank security and monitoring systems is helping improve surveillance, especially when it comes to storage and transport of cash. Visual artificial intelligence tools can review video feeds that monitor locations, access points, couriers, vehicles, and exchanges of money; the video is analyzed to identify suspicious people, behaviors, timing, and patterns that may be part of a financial crime.

Preventing Financial Crimes with AI

As fraudsters update their schemes and structures for financial crime, the exposure to financial losses and data breach continues to widen. Compliance firms and teams globally must also improve their use of artificial intelligence to predict, prevent, and minimize fraud in progress. The development and depth of international fraud rings and cyberthreats puts financial institutions in a constant defensive stance.

Combining efforts from companies and government organizations can create collaborative efforts to quickly increase the value of these programs in deterring money laundering, terrorist financing, identity theft, and account compromises.

Click here for the 2nd part in our series: AI in FinTech - Wealth Management

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