Jwahar Bammi | Principal Solution Architect, FIS
November 11, 2019
Modern banking has evolved into an always-on, omni-channel operation. From opening an account, to checking an account balance, to making a purchase with a mobile device, customers expect a frictionless, secure interaction. Now that 70% of banking interactions are digital and newer innovations like real-time transactions have gained in popularity, fraud potential has increased. Yet, the amount of time banks can take to evaluate risk or act on threats has greatly diminished.
In addition to customer transaction security, banks must now have the capability to predict, prevent and immediately respond to the sophisticated financial crimes like money laundering, internal theft and cybersecurity breaches, across the entire enterprise. Over the last decade, financial institutions have been fined nearly $20 billion in anti-money laundering (AML)-related penalties. Fraud-based crime has cost banks as much as $183 billion, and cybercrime is estimated to have cost them an astounding $3 trillion. As importantly, banks that fall victim to financial crimes face potentially irreversible reputational destruction.
To gauge whether your financial crime management strategy is still appropriate and comprehensive given the demands of modern banking, consider these four questions:
Banks must know about threats immediately and take swift action across the entire enterprise. This demands a holistic fraud management solution that concurrently analyzes millions of records across every channel. This analysis must occur in real-time and instantly return intelligence around suspicious results.
Because criminals target internet and mobile transactions with different tactics, different channels and modes of fraud perpetration require analytical models that are tailored to each. If a bank’s solution doesn’t have all-encompassing, channel-specific analytical models built in, it’s vulnerable to financial crime.
Banks handle massive amounts of data that can be used to thwart financial crimes—but it must be usable and in an actionable format. An effective enterprise financial crime management solution must be data scheme agnostic, not reliant on storage databases (which can become expensive and unwieldy), and able to easily integrate disparate information sources. It should also analyze data links and visually present the information in a manner that’s easily interpreted.
Artificial intelligence (AI) and machine learning (ML) are highly powerful tools in predicting and identifying financial crimes, but they’re not one in the same. AI refers to machines carrying out tasks in a “smart” manner; ML is a subset of AI that gives machines access to data and lets them learn for themselves. Despite this, many enterprise financial crime management solutions cannot distinguish between AI and ML, which is necessary to effectively prevent and predict financial crime.
With highly scalable ML and AI capabilities, FIS Memento spots fraudulent transactions across your entire firm in real-time and predicts new threats. Plus, you gain all the cross-channel tools your staff needs to efficiently and holistically manage any threat. It’s just one of the reasons FIS has ranked #1 on the RiskTech100 list four years running.
Learn more about how FIS empowers financial institutions to outsmart fraudsters using state of the art technology here.