How Banks can leverage Data Blueprint to create an effective and efficient AML programme

The Anti Money Laundering (AML) space is like a double edged sword that dangles on Banks. While on one hand the regulations are getting more stringent, on the other hand, fraudsters are always finding loopholes in bank's processes, procedures and patterns to go undetected with their money laundering activities.

Hence, Banks need to up their efforts to outsmart such fraudsters by constantly updating and upgrading their money laundering procedures, patterns and business rules that can help identify potential suspicious transactions.

Banks are striving to enhance their AML compliance framework, and will continue to do so. However, Banks need to realize the potential benefit they can derive in this area, through effective use, of the vast amount of data that is available with them.

Data – it's available, but there are various challenges

While there is no shortage of data, Banks have to face their own set of hurdles to effectively leverage this data.

Let's look at what most Banks face in this area: 

1) Legacy / Stand alone systems

2) Data Silos and Inconsistencies

3) Data Quality

4) Legacy and Complexity of Rules

5) Budget Constraints

6) Skew towards Structured Data

7) Data Ownership & Lineage Issues

8) Understanding the True Source of Data

Most Banks operate on legacy systems, thus giving rise to data silos and inconsistencies in data. This impacts the quality of data, which in turn affects the insights that Banks can derive out of data.

Additionally, Banks are more comfortable dealing with structured data, thus leaving an entire world of unstructured data untapped and underutilized. This too impacts the insights that the bank can derive to increase the effectiveness of its AML compliance.

To top this, Banks face an issue with ownership of data, understanding the data lineage and incomplete/inaccurate data. These issues further compound the effectiveness of the AML programme.

The Data Conundrum needs to be overcome

Banks have various types and sources of data, like customer data, transactional data, alerts data, external data, reported data. Each transaction and interaction generates more and more data. And, thus Banks have to deal with the data conundrum.

What is required is good data – the quality needs to be top class to derive insights and present to regulators in an intelligent and effective way. Banks also need to understand the true source of the data, the impact that the data can hold so as to use the most appropriate data for its analysis and reporting.

And, the journey for collection of good data commences at the Know Your Customer (KYC) stage. In fact most challenges with data that is used for monitoring transactions actually arise due to ineffective KYC information gathering and update. For example, obtaining the accurate and most recent address and verification documents of a customer is critical at the KYC stage and keeping this information consistent and updated across various systems is the next step that Banks have to ensure throughout the lifecycle of the customer.

Any lapse in obtaining the right information can lead to an incorrect assessment of the customer, thereby creating a skewed risk profile and risk rating for the customer. This can cause an adverse impact on monitoring the customer's transections for money laundering.

Additionally, Banks face a challenge with disparate systems and platforms, thus limiting the end to end flow of data, and having to deal with data duplication. For example, a customer holding multiple accounts and conducting several transactions, will leave a trail of data like his name, transaction amount, type of transaction and other related information across the Bank's systems and applications.

Banks find it very difficult to consolidate this information in one place, thus preventing data duplication and efficient transaction monitoring.

How can Banks solve this conundrum?

Understand Data – its elements and attributes

To effectively leverage data for monitoring transactions, the AML staff needs to understand the critical data elements and their business context, as related with the AML process. For example, the definition of a currency code or country code is standardized across banks and their various units, while the business context of a customer may vary across banks and their units. Hence, the right definition of the element is essential to comprehend the data that will be used in AML.

Additionally, knowledge of the criticality of the data elements, along with the optimum usage within the process, the systems, databases and business rules that refer to these elements is of utmost importance too.

For an efficient AML programme, Banks must determine the inputs required (data) and the output that this data will produce (true alerts; risk triggers, dashboard, KPIs, reports).

Banks thus need to bring in a transformational revolution to use data effectively. And, this can be done through the Data Blueprint initiative that can be undertaken in the AML space.

Data Blueprint – lessen the chaos around data

A Data Blueprint is a canvas to define the critical data elements and their associated attributes to maximize gains and efficiency around the transaction monitoring efforts of a Bank. This initiative helps the Bank to categorize data into groups, as per the criticality and the output expected from it. The blueprint enables creation of a data sandbox, which can be leveraged to run an effective analytics programme for AML. 

A Data Blueprint brings in a world of opportunities to understand and leverage data, and creating one for AML related activities will help banks leverage data and work on creating efficiencies through thee insights derived, So, let's take a look at what are the important data elements in the area of transaction monitoring

The AML Blueprint

The first step in creating a data blueprint for AML is to define the data elements and the attributes. For example, if the Customer is a Data Element, the attributes for the same would be : Customer Type, Customer Segment, Customer ID, Customer First, Middle and Last Name and others. Another example is Transaction as a Data Element and the linking attributes like inflows, outflows, transaction ID, transaction type, transaction description, transaction date and time, among others.

Once the repository of important data elements is created, the Bank can create specific lists of data elements and attributes in an area wherein they face a challenge. For example, if a Bank faces challenges in regular update of a customer's risk rating, as per the changing circumstances of the customer, an analytical model that can highlight such triggers, will help the Bank to amend the risk rating, if required. The Bank can create a repository of critical data elements that will be used is such a scenario.

A Data Blueprint equips the Bank to sift through the tons of data that is available, and identify the critical data that can be leveraged to build powerful solutions and derive insights that lead to business benefits (like reduction in False Positives or improving automation in transaction monitoring or improving the effectiveness and efficiency of the AML operations).

Banks should incorporate the Data Blueprint concept as part of their AML strategy. Using the right set of data to improve operations and monitor transactions for money laundering, will ease the pressure of this activity and provide benefits that impact that positively impact the Banks bottom line.