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Cognitive Automation in Governance, Risk and Compliance

Oleg Vilchinski

VP, Co-Head NA/East, EPAM US
Blog
  • Business Information Services

As data-driven decision-making becomes ubiquitous within governance, risk and compliance (GRC), many organizations are turning to big data and cognitive automation solutions to not only understand past trends and losses, but also to predict future risk postures or automatically remediate incidents to reduce losses at enterprise scale, globally.

Cognitive automation is beginning to play a significant role in augmenting human judgement to minimize human error as well as enhance assessment and decision-making. Some examples of trends that are quickly gaining popularity in this space include risk analytics, threat intelligence and continuous control monitoring.

Leveraging different technology approaches such as natural language processing (NLP), text analytics and data mining, semantic technology and machine learning, cognitive automation is fundamentally based on Robotic Process Automation (RPA) and extended with Artificial Intelligence (AI). Within risk and compliance, RPA drives cost efficiencies and reduces manual and remediation efforts, allowing organizations to meet stricter regulations and tighter deadlines. In fact, organizations can achieve more than 50% in savings for full-time equivalent (FTE) activities and 40-60% cost reductions for related activities.

Here are a few compelling risk and compliance use cases for cognitive automation adoption in the areas of risk monitoring, risk controls and risk reporting:

  • Anti-money laundering (AML) alert investigation. Most aspects of the processes for researching and resolving anti-money laundering alerts are manual or semi-automated in nature, and are therefore a prime candidate for cognitive automation. A combination of special analytics and behavioral and risk monitoring techniques helps organizations investigate system-generated alerts with improved detection accuracy and prediction patterns of suspicious activities.
  • Know-your-customer (KYC) onboarding. During the know-your-customer onboarding process, connecting disparate data from many internal systems and external sources is a challenging task, especially since the data is mostly unstructured. This is another area where cognitive automation can be effective. For example, automatically collecting and retrieving information from regulatory agencies such as the SEC and law enforcement agencies can support and drastically improve the onboarding process. Some organizations have already implemented cognitive automation on KYC processes like document gathering and validation. This allowed for a 1-2 minute processing time instead of the hours or even days required to collect, review and update data.  
  • Internal and external reporting. For many organizations, gathering data as well as creating internal and external regulatory reports are highly manual processes that take time from other important focus areas. Daily liquidity coverage reports and delinquency reports, for example, are often prepared manually and can be easily automated through cognitive automation, reducing processing time by more than 50%.
  • Limit management. In the limit management process, limit breaches or violations are reviewed and closed by risk officers. Since the resolution involves assimilating both structured and unstructured data sources and analyzing the data, cognitive automation solutions help to automate the limit management process and make it cost efficient.
  • Reconciliation. Reconciliations occur at many levels and form a key precursor in internal and external management information reporting. Most of the processes in the reconciliation process, especially in the area of data gathering and preparation, are manual and are another great example of a case for cognitive automation.
  • Stress testing. Comprehensive capital analysis and review (CCAR) stress testing processes involve the aggregation/netting of multiple lines of business revenue and expenses for reporting and forecasting. Most of the processes involved in reporting and forecasting are typically manual, which increases the risk of error due to human factors and limitations of data. Cognitive automation eliminates human errors by removing manual steps, as well as provides more accurate multi-scenario analysis, and forecasting based on massive and detailed statistics data and mathematics algorithms.  

Overall, businesses are drawn to cognitive automation because it promises new efficiencies for enterprise application while improving the quality of business, operational scalability, turnaround and error rates. In fact, companies are achieving ROIs of up to 300% in just months after implementation.  Across every industry, organizations are putting cognitive automation at the core of their digital transformation strategies. Are you ready to do the same?

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