4 ways artificial intelligence is transforming compliance for banks
Verifying customer identity sounds straightforward, but despite the simplicity of the concept, Know Your Customer (KYC) compliance has become one of the most complex, consuming, confusing and costly processes for modern-day financial institutions.
In the US alone, anti-money laundering (AML) compliance costs for financial institutions are a sky-high $25.3 billion annually. Of course KYC is a subset of this, but despite significant spend and staffing investments, KYC workflows are falling short of their desired outcomes. Heard of Danske Bank’s ongoing money laundering crisis? Or ING Bank’s $900 million fine for AML reporting failures? What about the “Russian Laundromat”? Cases like these aren’t going away. Rather, they’re becoming more frequent as regulations expand and enforcements increase more quickly than banks can adapt.
Customer experience and business growth are also suffering at the hands of lengthy KYC processes. Analysts sort through enormous volumes of internal and external data to identify customers and monitor for risky activity in line with regulators’ expectations. That takes time, requiring up to a shocking three months to onboard a new client — and some applicants aren’t sticking around. In the UK, it is estimated that 25 percent of applications are abandoned due to KYC friction.
Statistics like these are staggering, and more than a bit overwhelming, but there is a way forward to greater efficiency and reduced risk within the financial services industry. Already, artificial intelligence (AI) and automation are becoming the go-to options for addressing the troubles of modern-day AML and KYC, and regulators are encouraging the adoption of innovative solutions. The biggest draw is AI’s ability to interpret, synthesize and correlate vast amounts of data, a game-changing contribution to the ongoing battle against financial crime.
Fashionable or functional: applying AI to KYC