Banks’ compliance departments are under increasing pressure: They face deeper scrutiny from authorities and must adhere to an increasing number of laws and regulations. Increasing criminal activity, cross border transactions and the complexity of dealing with huge quantities of data further adds to the challenges in dealing with the situation. A combination of automation and machine learning is an approach that can enable banks to meet these challenges successfully.
Compliance: A critical factor in the financial sector
Since the 2008 financial crisis, the issue of compliance and risk management has become the topmost priority for regulators and hence, the highest levels of management in the financial sector. We have seen legislators globally, passing an ever-increasing number of laws and regulations that banks must adhere to.
For instance, the European anti-money-laundering guidelines, the EU-US Privacy Shield, Market Abuse Directives I & II and the associated Market Abuse Regulation are some of the prominent directives. At the core, all these measures intend to combat fraud, money laundering, terrorism financing, tax evasion, and market abuse.
Meeting prescribed requirements are not only necessary for survival but have also become a source of competitive advantage for financial institutions globally.
Risk management under pressure: Impending penalties, increasing quantities of data, and a skills shortage
Conventional means are no longer adequate to fulfill the increasing requirements. In addition, an acute shortage of compliance experts and IT specialists makes it even more difficult to deal with the situation. To top it all, growing cost pressure in a volatile market further aggravates the issues faced by financial institutions. Banks have no choice but to act now and optimize their risk management systems.
If internal control mechanisms fail, penalties on the financial institutions can include:
- Sanctions and fines amounting to millions
- More systematic prosecution
- Detailed audits
- Damage to reputation
Solution: Automation and machine learning
The solution lies in the skillful combination of digitalization, automation, and machine learning. These methods can analyze huge quantities of data more efficiently, detect suspicious patterns more easily, and recognize potential risks early on.
The special success factor is the combination of prescriptive approaches (business rules) and predictive approaches (machine learning). The benefits of this approach are:
- Codification of compliance criteria as business rules by bank experts
- Automated checking of huge quantities of data by special software
- Machine learning: Stores knowledge from experience, learns autonomously, and finds solutions independently.
- Highly efficient Big Data analysis identifies unusual transactions more easily
- Efficient detection and prevention of money laundering, fraud, and terrorism financing
- Legally compliant implementation of and adherence to compliance requirements
Summary: Machine learning as a success factor in banking compliance
Without doubt, the use of machine learning and automation are invaluable tools, enabling banks to fulfill rapidly growing compliance requirements. The self-learning IT systems can perform complex tasks such as fraud prevention, detection of money laundering and market abuse much more quickly and efficiently than previous methods allow. They reduce the cost and effort involved in complex analysis and checks, increase efficiency, and lower costs.
Find out more in the extensive white paper entitled “Why successful banks are relying on Machine Learning in issues of compliance”, which you can download for free here.