AI Driven Solution for Credit Risk Assessment Based on Climate Change Factors

Our data science team develops a machine learning model to be integrated into the credit lifecycle management system to improve risk management for financial service providers.

About the Client

YAPU Solutions is an innovative FinTech startup for farmers in developing countries. Due to various climate-changing events caused by human activity impacting weaker rural markets, YAPU recognizes the urgent need for resilient financial solutions that would help small land-holding farmers to streamline production by increasing working capital.

Unicsoft has already built a credit lifecycle management system for YAPU. After a trial run, the client is looking to load it with AI-powered modules that enhance risk management and improve operational efficiency for financial institutions.

Solution Highlights

YAPU’s cutting-edge system is capable of lowering credit default for financial institutions due to delivering a better view on climate- and nature-related risks in credit assessment. The solution aimed to provide data insights being credit risks and cahflow projections based on analyzing a spectrum of factors, including locations, climate factors, soil quality, ability to cultivate crops, etc.

Business Goals

YAPU wants to power up its existing system and observe the impact of applying ML to predict the probability of a farmer going into a credit default. Farming credits aren’t like traditional loans. There are a number of different qualitative and quantitative risk factors to measure before financing high-risk credits for small farmers.

With this in mind, YAPU came to Unicsoft to work on the following machine learning applications:

Credit risk assessment

YAPU wants to power up its existing system and observe the impact of applying ML to predict the probability of a farmer going into a credit default. Farming credits aren’t like traditional loans. There are a number of different qualitative and quantitative risk factors to measure before financing high-risk credits for small farmers.

Expected results

Financial institutions will receive more detailed credit risk assessment reports basing on several data sources and comprising credit ratings, sector & activity risks, cashflow projections. As a result, financial service providers will be able to make more objective and data-driven decisions, accelerate credit disbursement process, lower credit defaults.

Matching engine for reference cards / Established taxonomies

A manager must manually choose and attach reference data cards to each new loan questionnaire. This is a lengthy, uncomfortable and error prone process since there are a number of different reference cards that can push managers toward faulty decisions. On top of that, humans are far less accurate than ML-powered matching algorithms, and lack automated approaches to review questionnaire relevance.

Expected results

With a dedicated ML module, the system will be able to automatically match reference cards with questionnaires, analyze mistakes and data accuracy, and give recommendations for improvement. Such an approach ensures the credit process automatically includes verification of investment, together with productivity and resiliency benefits.

Climate-related solutions

The agricultural industry revolves around conditions in nature, in particular the climate. Increased temperature, changes in precipitation, and even increased CO2 content can affect crop production. As a result, farmers won’t achieve their projected profits and may fail to repay a loan.

Expected results

The new ML model will analyze all the major climatic factors affecting crop production and come up with a solution to mitigate those risks. When preparing a loan questionnaire, the system can advise securing additional lines of credit for meeting the climate needs that may arise in the future, thus helping improve credit scores.

Enhanced operational efficiency

Banks have been in the business of deciding who does and doesn’t receive credit for centuries, but with ML-powered modules, this traditional process is set to transform into a trustless system. With that being said, credit approval is just half of the battle for banks.

Expected results

Machine learning can minimize the risks of loan repayment failure by monitoring employees time spent on tasks, credit statuses and other team productivity metrics. Thus, C-level will get a detailed view on the team’s performance.

Project Scope

Unicsoft has been involved in the process of integrating AI functionality into the existing credit management solution from scratch.

We have conducted a feasibility study that involved building a comprehensive report on all data use cases, defining what data needs to be gathered & processed, and deciding on algorithms for future model training.

 

Currently, Unicsoft works on covering the first use case of credit risk assessment following the established ML model development process:

  • defining business requirements
  • feature modelling
  • analyzing available data
  • model’s performance evaluation & optimization
  • collecting & preparing data

Expected Outcomes

Future ML modules will be seamlessly integrated into the existing YAPU system and resolve the challenges mentioned above. These modules are projected to enhance the credit risk assessment system, optimize credit scoring & management processes, and make the system more efficient to use.

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