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Challenges from financial crime incidents and penalties grow
Criminal enterprises are becoming increasingly sophisticated at the same time as new payment methods are emerging (peer to peer and faster/immediate payments) and transaction volumes are exploding. And financial institutions across the globe are feeling the pain of high-profile financial crime incidents in the form of significant losses and regulatory penalties.
“As transaction levels continue to rise, so will the number of analysts and investigators needed to sustain adequate standards. This leads to higher costs without a corresponding increase in return.”
This is forcing many financial service organizations to spend even more on financial crime management, including systems and staff, to meet the rising number of threats and continuously growing regulatory scrutiny. Institutions must stay apace of best practices and keep pace with their peers in order to demonstrate they are meeting their responsibilities when it comes to anti-money laundering (AML) compliance, payment fraud prevention and identification of employee conduct issues, from insider trading and market manipulation to sales practices and policy violations.
Changing views on AI are an opportunity for improvement
As I’ve noted in previous blogs, attitudes toward improving the way financial institutions handle financial crimes are changing. Regulators in the US and across the globe are supporting and even encouraging experimentation with new ways to handle financial crime detection, operations and management. This includes the use of artificial intelligence (AI), advanced analytics and robotic process automation (RPA).
In turn, many financial institutions have already begun exploring how they can improve the still time-consuming, labor-intensive and inaccurate processes of alert triage, enhanced due diligence reviews, payment fraud prevention and modeling and conduct surveillance investigation.
Your peers are already exploring the benefits of AI
Risk.net conducted a financial crime survey for IBM in January of this year. Responders were financial services professionals involved in financial crime analysis and investigations, including AML and customer due diligence (CDD). The results showed that banks are learning the benefits that AI and cognitive can bring to those areas of the business.
Most respondents (64%) in the study felt that alert triage and risk prioritization could be improved by AI and cognitive capabilities, followed by enhanced CDD (57%), and automated alert disposition (56%)*.
But the most important statistic was 87%. That’s the percentage of respondents that are evaluating or already using AI in their anti-money laundering programs alone. As regulators see the initial application of AI in some firms, they will begin expecting it at others. In fact, there are already some financial institutions that have been asked by their examiner why they haven’t begun using AI.
Fighting financial crime with AI, the IBM whitepaper, presents more areas of this research study and includes both applications of financial crime technology as well as the initial results we’ve seen from financial institution clients.
The biggest challenge may be where to start
In most of the conversations I’ve had with financial institutions, there is little doubt that AI and automation can have a benefit. The hardest part of the decision is where to prioritize among their current operational challenges.
Current systems, especially those that have been in place for decades, were not designed to handle the complexity and scale of the modern financial system. There is understandable interest in improving these financial crime processes without disrupting daily operations, and these solutions are designed to do exactly that. But among the dozens of systems and processes, what should be highest priority?
My advice is simple: Stop waiting and start now.
Whether you pick the simplest use case to generate quick results or would see greater value from modernizing your entire investigation process, you will only see the benefit if you act. Now that regulators have removed the uncertainty around their views on innovation in this space, it’s up to financial institutions to follow through and start competing on effectiveness, not just efficiency.
We already have several use cases that show how these capabilities can identify false positives and generate explainable insights to improve operational efficiencies and speed up investigations. There are also some great examples of detecting suspicious or fraudulent payments more effectively across enormous scales of transactions.
Read the IBM whitepaper, Fighting financial crime with AI ,here.
Learn more about IBM regulatory technology ibm.com/RegTech
*Risk.net Survey Report “Smarter thinking around financial crime prevention”, January 2019