Enriching Data And Analytics To Strengthen Bank Compliance
Source Contributor on Leadership Strategy at Forbes.com
Innovation in technology and the digitization of information facilitate the rapid flow of data and money to and from anywhere in the world. Unfortunately, while there are obvious upsides and benefits from these enhancements, there are also new risks and challenges. Bad actors use the same tools in the global banking system to launder money to support activities ranging from organized crime to terrorism, drug and human trafficking, resulting in increased complexity for banks to invest in anti-money laundering (AML), know your customer (KYC) and other compliance-related initiatives.
The cost and complexity of these operations have raised regulatory and reputational risks as well as cut into banks’ profitability. Unfortunately, there is no short-term or simple fix to identify and address these vulnerabilities, especially for legacy institutions with vast branch networks, large numbers of employees, and multiple points of access for would-be wrongdoers. And new entrants to the industry are starting with simpler, next generation infrastructure solutions that are easier to protect, potentially putting pressure in the future on the incumbents and their legacy back-offices.
The industry is moving to embrace more analytics-powered monitoring of financial crime threats, where KYC and transaction monitoring risk signals are generated by algorithms and technology to provide the risk professional with more insight into the threat and access to more complete sets of information to make a decision. One of the key issues that banks face in advancing in this direction is the lack of readily available, high-quality data for use in KYC and AML decisions. This is further complicated by the sheer volume of transactions, as well as the challenges of managing the cleanliness, comparability, and consistency of the data in an ever-expanding data universe.
To be effective in spotting the most suspicious and risky transactions, data needs to be drawn from a combination of internal and external sources, while making better use of automation and analytics to recognize outliers across the data sets — without getting bogged down in significant volumes of false-positives, which consume analysts’ time on the wrong transactions. Institutions are increasingly applying these new technologies while incorporating unstructured or third-party data — in combination with deep learning and artificial intelligence – to help identify suspicious transactions and prevent financial crime.
More and more banks are making use of techniques such as sentiment analysis and relationship mapping –both based on the high-speed examination of millions of pages of content in global media, court filings, web pages and social media — to get to the bad actors faster. One example is development of intelligent networks, which monitor data streams in real time and in multiple languages to identify suspicious activities and relationships, specifically seeking out accounts, companies, individuals and aliases with known ties to fraud as an indicator of high risk for seemingly innocuous transactions.
Once data is collected, data visualization can make complex data sets and analysis much more understandable for the analysts, mapping out and displaying connections which might otherwise have gone unnoticed by humans with more traditional tools.
While solutions employing machine learning and AI can spot new indicators and patterns of behavior linked to money laundering or other non-compliant and suspicious behaviors, such solutions can also help cut the cost of Know Your Customer and Customer Due Diligence (CDD) programs, which can be hundreds of millions of dollars per year for large institutions.
Successful implementation of solutions using analytics and third-party data provides banks with an even bigger benefit. Banking industry leaders have come to recognize the importance of building (and, in some cases, rebuilding) customer trust, which has been damaged by the combination of data breaches, money laundering scandals and marketing misfeasance. In this environment, banks can differentiate themselves from competitors by using the most advanced technologies as part of a comprehensive effort to improve regulatory compliance while protecting their customers’ funds and data, as well as their own brands and reputations.
Join TCI at the Evolution of Data and Analytics Conference 2019, where the journey of data to cornerstone information will be discussed for the purposes of giving your orgnaisation the cutting edge of the Southern Africa marketplace of data professionals.
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