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AI/ML Application Cases

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Artificial Intelligence (AI) in BPM is ideal in complicated situations where huge data volumes are involved and humans need to make decisions. Machine learning is the part of artificial intelligence (AI) that focuses on the development of computer programs that can access data and use it to learn for themselves.

Comidor platform offers the ability to build your own Low-Code App through App Builder, and include both AI and ML components, in order to determine the writer’s attitude, get predictions, classify text and enhance digital process automation.

In this article, we will give two AI/ML application cases of real business problems where we have included AI and ML in the solution.

Case 1. Loan approval process

Business Problem

A loan approval process starts when a potential borrower reaches out to the organisation. The first-phase employee should input all customer details and check the customer’s creditworthiness. In the next phase, a second-level employee should review all data and decide whether to approve or reject the loan request, which might be demanding, of high-risk, and time-consuming especially for a new employee.

There was not a central system that could handle and manage all loan requests and process steps. The main need in this case was to enhance the accuracy of the decision-making process.

The solution

As a solution to the above, Comidor offers a Low-Code application to monitor all Loan approval processes in one place along with a workflow that orchestrates all process steps.

ai ml cases | Comidor Digital Automation Platform

In the workflow we have included:

  • Public form for process initiation by the potential borrower outside of the Comidor environment
  • Task allocation to the responsible users and groups
  • ML Predictive Model that predicts the loan approval decision based on historic data and variables such as the annual salary and credit score of the borrower

ai ml cases | Comidor Digital Automation Platform

  • User forms & fields for data input and display
  • Gateways and conditions for path determining
  • Automated emails
ai ml cases | Comidor Digital Automation Platform
The Loan approval process steps in detail are:
  1. The loan request process is triggered by the customer on their personal web banking portal, with Comidor embedded public forms. The customer adds personal details and the loan information, and selects the type of loan and loan interest.
  2. The first-phase employee is notified about the new Loan request, reviews it and adds further information (Credit score)
  3. Based on the predefined range of variables in the loan process and historical data on the approval process, the Comidor ML Predictive model provides an instant, high-confidence
    suggested decision.
  4. Then, the next-level employee is informed about the loan request and the available ML prediction. The employee can then take the final approval/rejection decision.
  5. Finally, the customer receives an automated email with the final decision about the loan request.

What we achieved:

  • Big data analysis
  • Robust credit decisions within minutes
  • Automation of the loan request process
  • Pattern identification
  • Human error elimination
  • Improved and faster risk assessment
  • Customer-Self service

 


Case 2. Customer request management

Business Problem

The Customer request management process starts when a new customer need rises. In this case, there are 4 types of customer requests: individual, corporate, support and complaint.
There was a lack of one central channel of communication between the company and its customers. Resolution time could take too long due to the huge volume of requests and therefore, complaints were increased.

The solution

For this business problem, the solution is a Low-Code application to monitor all Customer requests in one place, along with a reporting dashboard. A workflow that orchestrates all process steps is also included.

ai ml cases | Comidor Digital Automation Platform

In the workflow we have included:

  • A public form allowing non Comidor users to trigger internal processes
  • Automated emails with process details
  • ML text classification model that assists in request categorisation
  • AI Sentiment analysis that analyses customer’s sentiment
  • Scripts to change the priority of the request upon certain conditions
  • Task allocation to the responsible users and groups
  • User forms & fields for data input and display
  • Gateways, conditions for path determining, and loops
  • Timer for auto-closing the process after a certain period of time

 

ai ml cases | Comidor Digital Automation Platform

1. Customer request initiation
  • We have added a Comidor public form to our client’s website so as to allow non Comidor users to trigger Customer request processes. The public form is an embedded form similar to the initiation quick add form in Comidor, including all user fields and business rules such as customer request details. Once the customer completes the public form, a new process starts in Comidor.
  • Alternatively, a Comidor user from the customer service department can manually trigger the same process within the Comidor environment, in case the customer places the request by phone, email or another source.
2. Process Flow
  • An automated email is sent to the customer confirming the receipt of the request.
  • Then, the ML text classification model makes a suggestion based on the customer’s request subject. The ML model has been trained with historical data to ensure the accuracy of classification.
  • An AI Sentiment Analysis model is used to identify and categorise opinions expressed in the request description and determine whether the customer’s attitude is positive, negative or neutral.
  • Based on the sentiment, the ticket priority changes accordingly, e.g. for negative sentiment, the ticket priority is set to top.
  • The Account Manager is notified about the ML text classification and the sentiment and then makes the final decision.
  • Then, the responsible department handles and resolves the ticket.
  • The Account Manager reviews the resolution. If the resolution is confirmed, an automated email is sent to the customer. If not, the ticket loops back to the department for resolution.
  • Finally, the Account Manager awaits for customer’s confirmation. If the customer agrees the ticket is closed. If not, the ticket loops back once again to the department for resolution.
What we achieved:
  • Real-time monitoring and reporting of all customer requests
  • Involvement of non Comidor users in internal processes
  • Lower resolution time with automatic request categorization
  • Increased productivity since manual steps have been removed
  • Better customer experience due to automatic prioritization


Find more information about AI/ML and Workflow elements that you can include in your workflows.

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