Why customer data is the common factor that complexified modern banking

Modern financial institutions have evolved from institutions requiring a significant amount of data to being dependent on it for most major strategic decisions. Over the last decades the rules of the game have changed. The calculation of a bank’s Key Risk Indicators (KRIs) have complexified to a point where every error made in the process can have drastic consequences on the interpretation and hence on the strategic decisions derived from such analysis.

The complexification of the calculation of a bank’s indicators happens across the entire institution, in every department and in every bank. The common factor for the majority of these indicators is that their data is based on what is and remains at the core of their business: their customers. This notion is key to understanding the importance of KYC practices in the creation and management of a bank's data. Institutions, of all types and sizes, who did not understand the extent of the shift in the importance of KYC data, face an ever-increasing risk of loss of competitiveness compared to the institutions who did.

How failure to incorporate the KYC data into risk and data management can affect the bank

Customer Due-Diligence (CDD) and KYC efforts required from financial institutions have risen at impressive rates over the last few years. In a period during which regulators are distributing record fines for Anti-Money Laundering (AML) and KYC breaches, the remediation measures implemented by banks should be seen as a competitive opportunity that could kill two birds with one stone, respecting regulatory requirements and enhancing the quality of key reports and indicators.

When it comes to the production of reports and indicators, which serve as key inputs into good decision making, small mistakes at any step of the process, from data-entry to calculation and analysis, can lead to misinterpretations or poor decisions. The best place to start to avoid these errors is the data collected through CDD and KYC. Customer data has the specificity of being the only dataset present at every level of a bank and which directly or indirectly impacts all the main KRIs.

Errors linked to KYC data commonly come from the following control or process failures:

1.     Correct data inputted incorrectly into the system (e.g., customer entering a new phone number in the address field)

2.     The data was not collected (e.g., the bank failed to follow up or collect new data required from current clients)

3.     Misuse or misunderstanding of the data (e.g., subsidiary’s economic activity mistaken with the parent company)

All of the abovementioned failures necessitate different levels of intervention to remediate. Whilst the first two types could seemingly be fixed by improvements in control checks and/or more stringent process rules, they both share the same root cause as the third example (i.e., the lack of understanding of the use of the data). The implementation and promotion of a strong holistic approach to data-culture within the institution can thus be a large part of the solution to fix all three types of errors.

 A correct and more holistic understanding of the use of data throughout the bank would help at every stage. From data collection (e.g., the IT department understanding why it is important to check for the validity of a phone number) to the data consumption (e.g., the analyst’s understanding how a subsidiary might be active in a different business line than its parent company). Not only should the importance of the data be understood at every level, but a point of attention should be made for the failure to prevent such errors. This should be underlined and made clear to the different analysts inputting and analysing the datasets, as such little effort can prevent big mistakes from being made.

For example, a simple error, such as the one showed in the third bullet-point, means a pension fund for the employees of an oil company could be considered as an oil company in and of itself. The consequences of such an error would not only affect the traditional reports, such as the risk appetite dashboard, but also rising topics such as ESG results. Another example would be a relationship manager not following up with an existing customer and failing to update the customer address after they moved to a high-risk country. This would directly affect the results of the risk calculation of the account. These types of errors can never be fully eliminated, but additional checks and clear understanding of how the data is consumed, helps to mitigate many similar errors and prevent incorrect conclusions.

It is highly recommended to be careful when dealing with customer data. Effective customer data management leads to better-informed decision making and could be used to develop a healthy risk and data culture whilst sending a positive signal to the regulator on multiple fronts.

Avantage Reply has a proven record of accomplishment in the management of data and risks across all levels and domains of the financial sector, including in KYC. Combined with its newly relaunched compliance practice, this ensures our capability to not only assist our clients with their challenges, but to be at the forefront of the conception and implementation of the innovative solutions required to overcome many of the abovementioned hurdles.