Curbing the Compliance Budget Exuberance: a case for AI to disrupt the trend of rising costs in compliance

Global financial services institutions spend as much as $100 billion each year on compliance. KYC due diligence and AML screenings are the largest items on the total compliance bill, collectively accounting for ~32%. Most of the spend is wasted on analyzing false positives. With Merlon, we were able to cut the number of false positives by 80% at one of the major private banks, which extrapolated to the global market would lead to potential savings of ~ ＄23 billion.

How costly is the compliance function?

About $4.38T (or 5% of global GDP) is laundered annually, according to the UN Office of Drugs and Crime. It’s estimated that the global financial services institutions spend as much as $100 billion each year on compliance, but are still suffering from massive regulatory fines when they paid over $300 billion in fines between 2010 and 2015.

A cost breakdown clearly shows the KYC due diligence and AML screenings as the major contributors to the total compliance bill; collectively accounting for ~ 32% of the total costs. That is more than 3 times the spending on external tech vendors and this mismatch unveils an opportunity for the financial institutions to cut the exuberant compliance costs.

The total breakdown is indicated in the table below:

Source: Uncover the True Cost of Anti-Money Laundering & KYC Compliance; LexisNexis

Most of the compliance spending is being squandered by inefficiencies in the operating model

The escalating operating costs and regulatory actions have not resulted in better compliance practice and compliance departments still suffer from their perennial pain points, mainly:

• non-standardized classification of materiality and perception of risk resulting in lengthy investigations; leaving the final risk decisions on analysts’ judgment
• poor data quality and multiple data sources with different structures and formats requiring costly manual updates, while making their integration into a single customer risk view almost an impossible challenge
• inconsistent processes, screening systems, and reporting that are complicating detection and mitigation of financial crimes
• accelerating product/service life-cycle creating hard times for compliance professionals to catch up with the latest releases and to set up the appropriate control measures

This all results in a broken compliance operating model producing an overwhelming amount of false alerts (False Positives), missing the illicit activities (True Positives) and dragging the business side (Customer Satisfaction).

False Positives

A daily reality of compliance analysts is screening and investigation of customers, who actually aren’t criminals but were wrongly flagged by the existing AML systems. In fact, the amount of false positives ranges from 75% to 90%.

Depending on the complexity of these alerts, a quarter of them needs to be reviewed by senior compliance analysts, driving up the cost per single alert up to ~＄20.

True Positives

Catching the “bad guys” using more or less sophisticated techniques to launder illicit money is the ultimate goal of the KYC/AML department. No human is perfect and compliance analysts are no exception from the rule. On average, the human error rate varies between 10% to 30%.

The failure to prevent financial crime ultimately results in massive regulatory fines. In the past 10 years after the financial crisis, regulators have levied more than ＄320 billion in fines globally. About 8% or ＄26 billion from this pie can be directly attributed to lapses in KYC, AML and sanctions screening. It is no surprise that the U.S. is a “leader” accounting for 44% on the total number of all fines and 91% on the total volume of fines globally.

Customer Satisfaction

The client onboarding process takes days to several weeks depending on the customer type and riskiness. The process itself requires between 8 to 10 interactions on average and in many cases contributes to the high drop-out rate of up to 90% (especially during online onboarding).

The equation is simple: the longer and more difficult onboarding processes are, the longer the time to value is. Moreover, with the burdensome KYC screening process increases the attrition rate particularly of newly onboarded clients.

Can it be tamed?

It is estimated that almost 42% of compliance costs is wasted by the global financial institutions on unnecessary false positives and other inefficiencies in their KYC/AML programs.

What’s worse, there is no visible sign of reaching the cost ceiling; according to the paper from Duff & Phelps, bank compliance spending could double between 2017 and 2022 and reach up to 10% of the total revenue. According to Deloitte, operating costs spent on compliance has increased by more than 60% since the financial crisis in 2008/09 .

Deploying the AI powered tech to rationalize the compliance spending

With Merlon, we were able to cut the number of false positives by 80% at one of the major private banks. If we would take this number and extrapolate it to the global market, the potential savings are ~ ＄23 billion spread across the major financial institutions.

Cutting the false positives means less analyst time spent on reading irrelevant articles and news. Saved man-days can be then either easily converted into labor cost savings or directed towards higher quality of the due diligence work on complicated AML cases.

More time for compliance analysts to conduct the thorough due diligence can prevent financial institutions from missing the money laundering, thus avoiding the regulatory fines. As the regulators gradually increase their scrutiny, the chances of being hit by the fine grow. This is a significant factor, especially when taking a closer look at the height of recent regulatory fines, that are ranging from ~ ＄16 million (avg. for small banks) to ~ ＄500 million (avg. for large banks).

Conclusion

The KYC/AML compliance function currently represents a challenge for every financial institution. It’s not only a heavy burden on budgets but potential failures lead to massive fines and damages to a firm’s reputation.

With Merlon and the advent of other machine learning Regtechs, we paved a new way for compliance executives to revert this situation and utilize the existing resources more effectively.

The rising number of AI powered compliance software being deployed across major banks shows that the market has finally overcome the initial fear from the new technology and the time for AI to make compliance more efficient is now.