top of page

Demystifying the impact of Money Laundering cases on Financial Institutions & How A.I can combat it!

Updated: Nov 28, 2022

In the last few years, we have seen several money laundering cases being exposed. Some of them include Panama papers, Baltic leagues scandal, and the latest being the Fincen files case that showed how several major institutions had engaged for years in handling up to $2 trillion in dirty money. This caused severe reputational damage and trust issues and shook the stock prices of almost all the leading banks; for instance, shares in Deutsche Bank, Germany’s largest bank, fell more than 8% [Reference: Dw].

Now, what exactly this means to financial institutions and what it means to its customers?

The public (customers) is much more aware of money laundering cases, its impact on the economy, and they are expecting larger transparency/trust from the Financial Institutions. There is a paradigm shift in AML compliance programs. They no longer remain just an obligation to comply with the regulatory guidelines, but there is much more to it now — winning the trust of the customers, investors, retaining the belief in the financial systems, transparency, and beyond. This further calls for effective and efficient AML Compliance programs among Financial Institutions due to its impact on reputation and its dire consequences.

Most of the leading banks are switching gears to consider adopting the latest technologies, and they are also well aware of the exploding false positive alerts they get out of the rule based AML legacy systems. The high rate of false positives cases is not just taxing to review for the workforce but also it comes with a baggage of heavy costs and time. Most importantly, these inefficient processes are leading to more unwanted alerts and putting additional burden on skilled employees who instead of focusing on catching adverse suspicious activities are spending time on trivial issues that result out of manual interventions. It is thereby leaving room to miss out on critical money laundering cases. Quite a few financial institutions started realizing this. They are transitioning to make the programs more effective and efficient. Besides, they are empowering investigators and the workforce with technology that would allow them to catch the true positive cases effectively.

Several leading banks are taking the approach of A.I to create efficient AML programs. For instance, “73% of Singaporean banks believe AI will strengthen anti-money laundering (AML) efforts.” – reported by cdotrends.

Going forward, a lot more banks will take up this initiative of enabling their existing programs with A.I to combat financial crime, money laundering, and terrorist financing.

Given financial institutions are under increasing pressure to maximize their organizations’ efficiencies while addressing anti-money laundering (AML) processes and procedures. Exacerbating that challenge is the need to meet increasing compliance requirements imposed by regulatory agencies with a workforce that has been disrupted by a global pandemic. At Complidata, we are proud to be solving this issue and creating an impact with our A.I platform that would empower several Financial Institutions to combat financial crime.

We look forward to being a part of this revolution. We shall continue our efforts in the areas of AML Transaction Monitoring, Trade Finance, KYC, Alert Risk Ranking, PEP & Sanctions screening, and AML Model Risk Management to help financial institutions significantly increase efficiencies in their processes and procedures while reducing the percentages of false-positive alerts.

26 views0 comments


bottom of page