Banks and financial institutions face may types of risks that include internal and external frauds. They are also subject to many regulatory compliance requirements, anti money laundering being one of them.
krtrimaɪQ has developed solutions using latest AI algorithms to address fraud and anti money laundering requirements of banks.
Mechanics of money laundering is getting sophisticated, which is becoming a huge challenge for Governments across the globe to come up with appropriate regulatory frame work. It in turn becomes a stringent compliance issue for the banks. Machine Learning algorithms are complimenting to traditional business intelligence and rules engine-based solutions to cope up with these new mechanics.
Banks spend billions of dollars in fines failing to comply with stringent regulatory reporting aimed at preventing money laundering and other financial crimes. Current anti-money laundering (AML) compliance processes are dominated by high levels of manual, repetitive, data-intensive tasks that so far have not been too effective in stopping money laundering activities, especially with emerging technologies and banking across boundaries.
AI technology is transforming the fight against money laundering by automating manual tasks/processes, reducing false positives coming out as alerts, and simplifying linkages across various players to establish financial crime. This helps in reducing the cost of compliance and improved accuracy of capturing money laundering transactions. AI can also identify more complex behavioral patterns that might otherwise be undetectable to the human eye.
At krtrimaɪQ, we help banks leverage AI technology to combat money laundering by improving compliance. Our AI-powered solution helps banks in:
- Identifying suspicious financial activity by account/customer with improved accuracy
- Establishes the linkages across various players involved in money laundering
- Regularly calculating risk of customers based on dynamically defined rules
- Staying compliant with regulations, even as they change
Once an alert is raised on an account or customer regarding suspicious financial activity, it needs to be probed to establish money transfers across a web of accounts, and shell companies. Graph DB based link analysis helps banks scan large number of different data pieces, uncover complex webs of evidence and discover financial crimes that are not apparent from any single piece of information. Map like visualization makes it easier to see the linkages across various players to establish illegal financial activity.
Link analysis helps banks simplify the analysis of alerts raised on financial crimes to establish linkages among various entities as evidence of the crime. This reduces the effort required to gather the evidence, is less error prone, and even non-technical business users can understand the visual representation of linkages.
Our Link Analysis solution uses Neo4j Graph Db for an intuitive representation of money movements, hidden relationships among people in terms of financial and demographics. Banks can uncover subtle or hidden relationships among entities that, when viewed at a network level, weed out crime and/or associated collusion.
Banks are prone to wide variety of financial frauds cutting across channels and product lines. They need to continuously upgrade fraud detection and prevention solutions to cope up with new fraudulent schemes coming into the system from time to time.
Traditionally business intelligence and rules engine solutions are deployed to detect and prevent frauds. Of late, sophisticated supervised and unsupervised machine learning algorithms are being deployed to compliment to existing solutions so that even complex and unknown fraud patterns can be detected and prevented.
These solutions are also being complemented with graph DB technology to identify fraud rings and complex patterns. Several banks across the world are deploying AI and machine learning solutions to detect fraud in real-time and improve fraud prevention.
KrtrimaIQ’s Fraud Detection and Prevention solution leverages traditional rules engine, complimented with supervised and un-supervised machine learning algorithms and graph DB based network analytics.