Keeping Pace with Technology

By Jenny Fung, Chief Compliance Officer, ABN AMRO Bank N.V

Jenny Fung, Chief Compliance Officer, ABN AMRO Bank N.V

With the exponential growth of using Fintech across the globe, behaviour of customers has been changed. More simple and efficient banking products or services are expected. This has forced banks to design new processes and develop new approaches in the provision of banking products and services in order to remain competitive, not only among the regulated financial institutions but also the non-traditional market players who may not be subject to the same regulatory supervision.

Having said that, the evolving and increasing complexity of regulations continue to focus on financial crimes/anti-money laundering/terrorist financing, investor protection, staff and market conduct. While banks’ obligations to comply with the regulations remain unchanged, the way of managing risk and compliance is no longer the same. In this context, Regtech being innovative technologies for helping banks to comply with the regulatory requirements and pursuing regulatory objectives in a more cost efficient way has become a rising solution to be adopted by banks.

So, how does Regtech help?

A raft of industry studies on the use of innovative solutions and the impact to the banking industry has been widely discussed. Examples of areas that are considered as largely benefited from the innovative technologies include:

• Anti-Money Laundering (AML)/Counter-Terrorist Financing (CTF)/Financial Crimes-Over the past decades, we have seen billions of regulatory fines imposed on banks that failed to implement adequate processes and controls in understanding and maintaining client risk profiles, assessment of the AML/CTF risk, transaction monitoring, name screening for identification of sanctioned designated parties or politically exposed persons. On top of this, the consequence of heightened regulatory requirements and global attention as well as the increasing resources invested to address the AML/CTF regulations across countries have resulted AML/CTF as one of the most costly and labour intensive area of regulatory compliance. This drives banks to search for Regtech solutions. Machine learning and nature language processing (NLP) are commonly adopted for identifying red flags of customers through checks against public sources, pooling customer data and identifying clusters of customers’ behaviour that are of higher risk, and comparing historical and predicted customers’ behaviour so as to identify patterns that require attention. This can largely improve the quality of screening and transaction monitoring results with higher degree of certainty, which at a result reduce the number of false positives and improve the decision making process of suspicious transactions. In addition, the ability to process and analyse real-time data can help banks to proactively prevent fraud/crimes instead of investigating them post event.

Banks are forced to design new processes and develop new approaches in the provision of banking products and services in order to remain competitive not only among the regulated financial institutions but also the non-traditional market players

• Client Suitability-This is another area of focus by regulators after the global financial crisis caused by mis-selling of structured products. Machine learning can facilitate banks to establish clients’ risk profile, not just relying on the questions and answers in the investment questionnaire, but also to assess the historical investment behaviour of customers. In addition, a more standardised process can be applied on product risk rating among banks. The building and maintenance of customer risk profile as well as matching with products by using Regtech are more efficient for banks to assess and prevent mis-selling risks, and provide more tailored products and services to customers.

• Market/Employee Conduct-Machine learning and NLP can be used to analyse unstructured data such as communication style of traders and trading pattern which facilitate banks to interpret those data and identify any abnormal behaviour or market misconduct such as collusion or manipulation. Further to this, AI is also being deployed to monitor internal culture and behaviours.

• Regulatory Interpretation and Reporting–NLP may be used to interpret new regulations and translate them into rules for risk assessment and reporting so as to help banks to comply with the regulations.

The above examples can show that, with the use of AI, the quality of information available for banks to manage regulatory risks is largely improved and decision can be made in a more effective and efficient way.

Are banks ready?

There are overwhelming ideas of AI solutions available in the banking industry and the expectation is high. However, it may be too early to say they have been successfully implemented. In reality, it involves significant initial investment cost, it takes time and it will subject to a new set of risks:

• Data privacy risk-This goes to another risk for how customer data can be well protected after they are on the shared platform, in particular the regulations on data protection vary across countries.

•Integration of legacy system-Banks with large scale and global coverage can develop their own AI solutions. However, can their legacy systems integrate with the new AI solutions?

• Data quality–Accurate and up-to-date data for AI to process and analyse is another pain spot by banks as it will take time and efforts to clean up the legacy data and categorise them properly.

• Outsourcing risks-It's common to adopt AI developed by vendor instead of in-house. But how can banks ensure the customer data in the vendors’ platform is safeguarded and able to understand the parameters and controls? As stipulated by the regulators, the onus of the outsourced activities is always with the banks. Can the individual obligations be properly discharged in particular the regulators such as, the HKMA and SFC are imposing more individual obligations on senior management.

• Cross border risk-The outcome of better understanding customers’ data and digitalisation of documents involve sharing of customer data across countries. Some country regulatory framework however does not permit outflow of data outside the country. Banks nowadays are no longer only subject to competition among themselves, but also non-traditional market players, which have direct impact on their earning sustainability if banks are not able to streamline their processes, broaden the variety of products and services, and operate at low cost. AI such as big data analytics or machine learning has transformed the way of compliance doing their jobs. It has saved a lot of time by reducing the manual and administrative process, and prioritizes the compliance resources on the risk assessment and strategic decision making.