While the rich world of chatbots is associated the most with developments of artificial intelligence, the ability of AI to mitigate risk remains one of the most important areas of development for financial institutions. And no wonder: even though the average support center call is estimated to cost $4.00[1] in the US, adding up to a significant expense if a support center receives hundreds or thousands of calls per day, it does not compare to the cost and a damage of various forms of fraud to banks, consumers, and merchants. The Ingenico Group[2] estimates that merchants lose on average 1.5% of their annual revenue to fraud attacks. This 1.5% represents product and service losses, chargeback fees, and potential scheme programs. Meanwhile, identity theft, fraud costs consumers more than $16 billion[3].

AI-powered risk management for financial institutions[4] is embodied in advanced fraud prevention and AML solutions, as well as more accurate customer assessment. However, AI has a more defining, and more rarely discussed impact – stress testing result submissions and the adjustment of capital requirements for institutions.

Stress testing submissions are vitally important for regulatory compliance. At the end of 2017, Ayasdi[5] shared the results of its work with Citi[6], one of the world’s largest and most complex financial institutions, operating in 98 countries, facilitating more than $4 trillion in flows each day. They hold over $950 billion in deposits and have over $620 billion in loans across their institutional and consumer businesses.

Explaining the background of the case, the company shares:

“The 2008-09 financial collapse led to a Federal Reserve directive that banks with consolidated assets over $50 billion have additional risk assessment frameworks and budgetary oversight in place. To assess a bank’s financial foundation, the Federal Reserve oversees a number of scenarios (company-run stress tests). Referred to as the Comprehensive Capital Analysis and Review (CCAR) process, these tests are meant to measure the sources and use of capital under baseline as well as stressed economic and financial conditions to ensure capital adequacy in all market environments.”

As Ayasdi reports, Citi consistently struggled to pass its annual stress test, failing two of the first three stress tests. The bank was in need of a way to rapidly create accurate, defensible models that would prove to the Federal Reserve that they could adequately forecast revenues and the capital reserve required to absorb losses under stressed economic conditions. The firm could not confidently defend the models that they included in the filings they presented to the Federal Reserve. And to address the issue, Citi chose Ayasdi to supplement its capital planning process.

Regulatory bodies are equally interested in adopting advanced technologies to tackle the issue. The Financial Stability Board (FSB) recently published a report[7] sharing that some regulators are using AI

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