Risk and compliance tools for the new tech and regulatory environment
Trade based money laundering has become more sophisticated with rapid improvements in technology and increasingly professional money launderers.
These proliferating tech/digital interfaces have made a large amount of information available in the public domain. By leveraging this information effectively, compliance professionals can mitigate risk of fraud.
However, new technology calls for new controls, failing which, banks and FIs would be susceptible to financial crime. And, big data has ushered in big challenges in terms of data quality and the need for data aggregation. Inadequate attention to these critical aspects can compromise the quality and efficiency of AML compliance.
Regulatory expectations are only bound to rise. How can banks and FIs adapt their AML programs to rise to these challenges? AI and newer analytics models show the way.
Networks of Relationships: Through the looking glass
For better insights into customer background and behavioural/transaction patterns, FIs should invest resources towards understanding the nature and dynamics of relationships between transacting entities. Current AML services running on relational databases are not inherently efficient at this. In a relational schema, it is a time consuming and expensive task involving complex joins and queries across a number of tables. These queries run up to days in some banks.
According to experts, graph analytics or network mapping is a better alternative that can improve the transaction monitoring capabilities of AML services. Graph analytics comprises nodes that represent entities – customers or accounts – and edges that represent the type of relationship between entities – payment, wire transfer, or a joint ownership. Querying in this model is faster compared to a relational schema. It is therefore better suited for both historical and real time analysis of patterns within a given set of transactions.
False alerts: Focusing on Quality
Banks currently follow a needle in a haystack approach towards threat detection. A huge 95% of alerts generated in every bank are false positives. Banks consequently employ a lot of resources to manually investigate these cases and close them out, which distracts them from the real threats. Lowering false positives can therefore enable greater savings and increase process efficiency.
By introducing advanced analytics capabilities between the stages of alert generation and review, compliance teams can better understand the topologies of transactions and relationships between entities. This data in combination with internal KYC reports and information from third party databases, can thus add new perspectives and help uncover hidden links during the CDD process, reducing the number of false positives.
The bottom line
AI, Big data, Blockchain, and Cloud Computing have caused explosive changes in the financial world. The tech evolution, and the consequent amplification of risk, mean that AML compliance today requires a synergy between experts from the tech and financial words. Collaborative partnerships and information sharing mechanisms are essential to tackle the increasing regulatory pressures.
