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Data Governance in AML

Banks and financial institutions play a critical role in collecting and storing customers’ data. From the compliance perspective, however, disparate data sources and the lack of standardisation in the available data is making AML compliance more and more difficult. Large amounts of resources are being invested into building sophisticated systems that aim to make AML monitoring more effective and efficient.

Technologies like big data and analytics, artificial intelligence and machine learning are being deployed to solve specific business problems such as that of false positives, providing a unified view of customers, readily available data and ongoing due diligence. However, one of the key ingredients in the success of the new technology developments is that of the availability of quality data in its most usable form.

A strong data system helps financial institutions to arrive at better risk categorization of customers, track movement of money seamlessly, monitor complex transactions, have a global overview of customers and thereby improve overall compliance.

One of the key ingredients in the success of the new technology developments is that of the availability of quality data in its most usable form.

If data is secure, consistent, standard and is available on a single platform, institutions may find it easier to identify money laundering patterns as the movement of money through the organisation is universally visible.

Good quality data can also prove to be of tremendous value to regulators, through timely and meaningful reporting, as well as the ability to make more informed decisions. Regulatory supervision can effectively move to an insight-based model, vis-à-vis a risk-based model.


Data availability and usability challenges

AML compliance officers often encounter several challenges while dealing with data:

Data silos

Data is available within various different departments such as credit cards, or forex or private banking, as well as from external data sources such as credit bureaus or media sources. Bringing this data from disparate data sources on to a single platform so as to provide a complete identity of the customer has been a challenge that many banks have been grappling with.

Lack of standardisation

Even when disparate data sources are brought together, there is no standardisation in the way data is obtained, stored or made available from each of these sources. Integration of diversified databases is among the biggest challenges faced by banks.

Lack of authenticity of data

The authenticity of data, especially those gathered from external data sources is often difficult to verify. The amount of data absorbed from such sources – such as google, or published media sources – depends on the risk appetite of each individual financial institution.

Quality differences in new and legacy customers’ data

Data quality has been improved with the help of new technologies, especially with the advent of digital on-boarding. However, the quality of legacy data continues to be inferior and integrating it with newer data poses a challenge.

Good quality data can also prove to be of tremendous value to regulators, through timely and meaningful reporting, as well as the ability to make more informed decisions. Regulatory supervision can effectively move to an insight-based model, vis-à-vis a risk-based model.

Addressing Data Quality Issues

The biometric system Aadhar introduced by the Indian government was paving the way for a unified system of identification serving as a national identity document for Indian banks, doing away with the need for multiple documents. However, the recent Supreme Court judgment striking down some provisions on the use of Aadhar is a huge set-back for Indian banks and financial institutions. It is expected that the regulators will soon bring some changes to existing laws and provide more clarity in terms of expectations from banks and financial institutions on the use of Aadhar.

Banks and financial institutions have been trying various innovative solutions to address data management and quality issues. Creating data warehouses or data lakes by integrating data obtained from various sources is one such area. Existing AML systems can then be connected to the warehouse, which will help sharing of data in a structured manner.

The need of the hour is to be able to combine physical data with digital data effectively. A collaborative approach to data management involving credible third parties such as technology service providers, credit bureaus, telecom companies and the likes can also be used to bolster the quality of existing data. Any successful partnerships will result in significant benefits for all parties, including underserved consumers.

Going Forward

A significant regulatory change that may be expected in the near future is a Data Protection Law by the Indian government. The Sri Krishna Committee, headed by Justice BN Sri Krishna has submitted its recommendations to the government, including provisions such as categorization of data into personal, sensitive and critical categories, mandatory consent for processing of data and the customer’s right to be forgotten. If passed, the bill may change the way banks and financial institutions treat data going forward, and trigger off another requirement for data management solutions and innovative approaches to the storage and usage of data. What also remains to be seen is the impact of these provisions on the existing PMLA guidelines, which will be critical for AML monitoring. 

In the long run, what will drive an effective AML compliance programme is access to the complete and accurate identity of a customer in a single place. It is imperative for financial institutions to understand the importance of good data governance, and work towards establishing systems that will support in achieving this goal.

In the long run, what will drive an effective AML compliance programme is access to the complete and accurate identity of a customer in a single place.