Read this article about Cognizant Technology Solutions, one of our finalists, in the category Best Achievement in Operational Excellence to deliver an outstanding Value Creation or Innovation Execution Project, selected by the independent judging panel, for the 2019 Business Transformation & Operational Excellence Industry Awards program. Click here to read articles on other finalists.

KYC e-2-e transformation for Periodic Reviews (CLM)

One of the key transformation projects within the Cognizant transformation journey for KYC reviews process for a leading Investment bank was initiated in Q2 2018 with the purpose of streamlining the end to end Background verification process. Cognizant carries out ~7,000 periodic KYC reviews annually for institutional clients of the bank. As part of these reviews, client names, ultimate beneficial owners, directors and controllers are screened for sanctions, negative news and PEPs. As a part of the additional due diligence process, the counterparty names identified are required to be run on RDC GRID (External Data Management Provider) & Clients Internal Data management application for the purpose of evidence gathering.

Each of the risks identified by the Data Management Application are then scrutinized carefully to mitigate/highlight any risk posed by the Counterparty on the client under review. This being highly complex process was having a direct impact on Process Cycle time and was posing huge risk on Cognizant delivery.

To identify the core problems, we performed the Front to back process review by mapping Customer and Case journey through VSMs which has led to identification of key Problem areas and helped in identifying the desired future state of process. 

Background Checks AS-IS Process View & Key Pain Points identified

• Data Management Application throws 90% false hits leading to additional non value-added effort to qualify false positive hits.
• At present ~40-50% hits are duplicate hits with No automated process of identifying hits which are duplicated from Previous year review and reviewing those was cumbersome and time taking process
• Highly manual process where analyst reviews and qualify Hits on PDFs (200—2000 pages) which are extracted manually from Internal and External Data Management application
• Average Cycle time for Background check process is high (1 -3 days/case) leading to increase in overall lead time
• Analysts manually paste’s Sticky Notes to rule out hits on every page leading to error & Data quality issues
• No Automation to perform Rule based decision making on Hits identified

To-Be Process Re-imagined View

The end state Process is the right mix of achieving future state through Process re-engineering Multiple digital levers.

• RPA Extraction Bot (AA): The Bot performs the first stage of Background Checks which is bulk extraction of all the PDF Review files corresponding to all the connected parties of the client from Data Management Application for Current year Review.
• R Analytics tool: Performs Advanced text mining on PDFs highlighting comparative view of 2 PDFs and classifies data(Hits) into 3 Categories
• Hits duplicated under previous year review
• Incremental hits in Current Review
• Previous year Hits with Updated News
• R Analytics tool: Performs Decision making through Pre defined Rules built in the Analytics Solution and Qualifies Hits as True hit or False Positives with Qualifying comments against each of the hit.
• Process Re-engineering:
• Re-imagined the entire process by enabling analyst to perform only on incremental hits.
• Transformed the Qualification process into excel instead of cumbersome Sticky notes
• Ways of working: Taken exclusive sign off from Financial Crime (FCC) & Quality team to include this new process as part of SOP