Inspiration:

Every bank performs thorough due diligence before approving any loan to its customers. Time can change for the borrowers as they may get into potential financial stress as a result of unforeseen situations such as job loss, medical emergencies, litigation, etc. It is not possible for the banks to directly know about these situations with its borrowers. However, these situations leave some traces in the financial records of the individual. Banks need to identify these early signals and take proactive action to avert potential delinquency. Identifying these signals manually for millions of customers on an on-going basis is a very costly and time consuming process.

IDP addresses this pain by applying AWS Machine Learning to pro-actively identify customers that show signs of financial distress and are prone to delinquency. Delinquency Monitor built using Appian calls AWS Machine Learning model that is trained using historical data. It can identify signals of financial stress with current set of customers based on their recent financial history. The Delinquency Monitor triggers Appian process for customers with risk level above certain threshold so that the risk team can review and take proactive action to avoid delinquency.

What it does (Use Case Details):

Every Bank performs thorough due diligence before approving any loan to its customers. This involves rules driven by Credit Ratings, Annual Income, Liabilities, value of loan, value of collateral, history, etc. However, Times change and so do situations with individuals. Unforeseen conditions like Medical Emergency, Job Loss, Litigation, New Liabilities cannot be predicted at the time of serving the loan. These situations make an individual vulnerable to delinquency. Learn how you can leverage AWS Machine Learning and Appian to identify patterns of financial distress with customers and work to proactively avoid business risk. Uses historical lending data to train Machine Learning algorithm identify patterns and use it to assess risks with existing customers. Use Appian workflow to proactively mitigate potential delinquencies before they occur.

Key Functionality and Features:

Put historical delinquency data to use using Machine Learning and BPM. Identify and pick up early signals of financial distress before the borrower becomes delinquent. Use BPM for risk review and loan remediation to proactively avoid the delinquency before it happens. Following are key highlights of the solution:

  1. Machine Learning model trained using historical data
  2. Periodic review of risk for all customers
  3. Identification of Delinquency Risk using Machine Learning
  4. Customization Delinquency Process for review and action by Risk Team
  5. Driven by business rules for overriding AI predictions
  6. Dashboards for in-depth visibility

How it was built:

Appian with AWS Machine Learning.

Accomplishments that we are proud of:

We are proud of leveraging Appian's latest features and functionalities along with AWS Machine Learning to help predict risk in this data-central approach that will help banks get ahead of a potential financial risk.

What was learned by the team:

Training Machine Learning Algorithms Identifying Patterns for Machine Learning Increased Knowledge of Machine Learning

What's next for Intelligent Delinquency Prediction:

Market to the Financial Industry

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