How False Positives in AML Systems Impact Profitability for Banks and FinTech Firms?
Anti-money-laundering (AML) rules need to be followed by banks and financial technology (fintech) companies. These systems are designed to look for abnormal money flow that may be a possible linkage to terrorism or drug money. But these systems also sometimes flag legitimate transactions. This is known as a false positive.
They cost the business time and money to prove that they are fine. False positives must be examined by banks and by fintechs to ensure compliance with the rules. This slows down customer transactions and pulls employees away from what they are meant to focus on.
There are also costs associated with operating the AML systems and paying staff to review false alerts. These additional costs wear down on the margins of both banks and fintechs. They rely on making profits in order to be able to function and to survive in the long run. If worked, high false favourable rates from AML systems can challenge profitability and sustainability.
The Costs of False Positives
Every false positive in AML systems must be investigated. Transactions that were incorrectly flagged require employees to review the transaction and ensure that no illegal activity is occurring. This results in banks and fintechs investing expensive resources.
High false favourable rates make compliance with AML regulations costly. Firms may have to shell out additional money to upgrade systems or personnel to take care of the mass of false positives. This rising spending affects the downward trajectory of profit margin.
Impact on Customer Experience
When an AML system flags a customer’s transaction as suspicious, it causes delays and frustration. Transfers, deposits, or withdrawals are put on hold while the alert is checked. This bad experience can harm a business’s reputation.
Customers may close their accounts and go to another firm if false positives in AML systems happen too often. Less customer activity means fewer revenue opportunities from account fees or investment products.
Effects on Revenue and Growth Potential
With transaction holds and reviews from AML false positives, banks and fintechs can only grow their business. They miss out on potential new revenue when unable to onboard new customers right away.
Existing customers may do fewer transactions if deals are slowed by too many false positives, clogging up the AML system. Very cautious detection settings that generate many false positives can hurt a firm’s ability to make the most revenue and investment opportunities over time.
Total Cost of Ownership of AML Systems
Besides software and licensing fees, the total cost of an AML compliance system includes costs like initial setup, ongoing maintenance, training employees, and managing false favourable rates. A study found the total average yearly cost of AML systems per financial institution was about $3.5 million.
A big part of these costs comes from staff time spent looking into thousands of potential false positives each year. Banks with higher false favourable rates spend millions more per year compared to those with systems that better reduce false favourable rates.
Measuring False Positive Rates Between Solutions
A Deloitte case study cited a single sizable regional bank whose existing AML system was kicking out around 8,000 possible alerts a month, of which roughly 75% were expected to be confirmed as AML false positive rates if reviewed. This created confusion among employees and hindered productivity.
This followed an alternative provider testing process, in which they selected a new solution with machine learning tools that reduced their AML false favourable rates by almost 30%. This freed up their compliance staff to look into the actual fishy behaviour. This range means that one solution may incur well over 50% more in false positives than another, resulting in a higher total cost of AML compliance as a result of false positives. Given this, systems that are able to cut AML false positive reduction represent significant savings in ongoing operational costs to firms.
How to Reduce False Positives with Technology Automation in AML
As the costs of AML are not only high but ever-increasing, both banks and fintechs are hungry for an effective way to diminish the number of AML false positives and reduce the number of alerts that do not need attention. Advanced technologies such as machine learning and artificial intelligence are slashing the level of false positives that conventional rule-based AML generates.
These advanced analytics tools can understand what a typical transaction looks like and can learn over time to recognize low-risk customers. This gives the systems the ability to focus more on what may be suspicious and requires investigation rather than having transactions that are false positives.
Key to Profitability and Sustainability in Banking
This is an expensive cost to the bank in two main ways:
- Lost resources from employees investigating false positives
- The system resources used to identify them
The overarching cost of AML compliance is imposed on financial institutions. Firms with more sophisticated solutions to the AML false positive problem, which let them reduce false positives more in AML monitoring, shave millions of dollars off their annual costs versus companies bogged down under extensive false favourable rates. Recent innovations utilize machine learning and artificial intelligence to help reduce the inaccuracies of false alarms.
