WorldLink

ML/NLP for Risk Mitigation & Quality Control

Objective

Our Risk Identification and Quality control framework is an approach with machine learning and natural language processing to prepare enterprises to identify challenges proactively and address them on an ongoing basis.

Industry: Financial Services – Banking

Services: Technology Innovation

Technology: ML/NLP Analytics

Reduced overall human capital costs from thousands of work hours to a day to run the model and validate.

Reduced the time to action from weeks to hours.

Developed an enterprise-secure foundation and reusable core component model that is applicable across various use-cases.

Our Client

Our client, a Fortune 100 company, is a global leader in the financial services sector, renowned for its diverse range of banking and investment products. With a strong presence in numerous countries, it offers innovative solutions for consumers, corporations, governments, and institutions worldwide.

Challenge

Evaluate the Completeness of the Risk Mitigation Processes and identify missing required components.

Risk mitigation is a crucial component, especially for businesses with regulatory requirements. These include industries such as banking, pharmaceuticals, manufacturers, and other healthcare-related service providers. The violation of the specific industry specific violation will result in fines or other punitive measures but also will result in loss of Market confidence.

However, the preparedness to mitigate Risk is an ongoing business. Often, the risk mitigation process needs to be more comprehensive and contain adequate information to prevent Risk. This follows that new business processes or practice changes warrant updating the procedures to reflect the new reality. Unfortunately, more often than not, much procedural information is buried in the text that requires countless hours of human reading and evaluation, which is very error-prone. So it is widespread to lose control and focus of the Risk mitigation process and assess the process’s robustness until an unwelcome surprise shows up. 

Approach

Employ a Natural Language Based (NLP) system to conduct a structural analysis

  • Design a system for structural analysis of the description
  • Employ intelligence to evaluate the model-based outcome   
  • Develop a system with a GUI-based front end for batch and on-demand execution of the model.

Outcomes

  • Enabled identification of the problem areas under the process domains that contributed to Risk
  • Enabled the solution for a problem that was nearly impossible to solve based on human talent alone due to workload volumetrics, scaling issues of human resources and associated cost.
  • Accelerated QA efforts with higher accuracy and efficiency from sample batches to real-time streaming execution with reduced the human domain knowledge dependency.
  • Reduced overall human capital costs from thousands of work hours to a day to run the model and validate 
  • Reduced the time to action from weeks to hours  
  • Created a cornerstone of proven AI-driven automation success within the bank for further adoption across the bank.
  • Developed an enterprise-secure foundation and reusable core component model that is applicable across various use-cases within the bank as an accelerator and best practice framework for ongoing AI efforts.

Impact

  • Reduced overall human capital costs from thousands of work hours to a day to run the model and validate 
  • Reduced the time to action from weeks to hours  
  • Developed an enterprise-secure foundation and reusable core component model that is applicable across various use-cases within the bank as an accelerator and best practice framework for ongoing AI efforts.

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Issues (Risk) Correlation & Consolidation Analysis

Hybrid Cloud Analytics

Data Strategy Implementation

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