What would you like your AI agent to do? We will take it from there.
Talk to an Expert

Case study

A Machine Learning & NLP Based Credit Scoring System5.0 rating5 Stars Rated On Clutch

Industry

Financial Services & Banking

Services Used

Artificial Intelligence (AI), Data Warehousing, Machine Learning, Natural Language Processing (NLP), Recommendations and Insights

Region

Africa

Engagement Model

Agile Based Iterative Delivery

Function/Department

Financial Planning and Analysis (FP&A), Strategy and Planning

About Client

  • We created this system for an African-based SMB that provides small loans to mostly the people without banks. The company asked for a simple streamlining system for loan approvals based on the data source that is more credible than the credit bureau’s data. This streamlined system allowed them to expand their business without worrying about the credibility of loan approval.

Problem STATEMENT

  • Creating a robust credit scoring system with the help of Machine Learning (ML) and Natural Language Processing (NLP) to help SMBs like our client as well as the financial institutions to process loans in real-time without much of a hassle and with minimum default rate.
  • The most challenging part of this project was to find a reliable data source for the client that is better than Credit Bureau’s data. The second challenge we faced was defining the set of parameters for calculating the credit score in the system.

Solution

  • Developing a data source and introducing Machine Learning and Natural Language Processing to automate the generation of credit scores.
  • The basic solution to what they needed was to build a data source that is not available on any traditional channel like Credit Bureau. So, we built a system on the basis of mobile device data which included extraction of data like income, investments, expenses, type of mobile devices, installed apps, etc with the help of NLP. This information accumulated by the NLP is then passed to the credit score system where the parameters were set. Due to the high volume of the data, this process did not work for long. To resolve this issue we introduced Machine Learning to automate the generation of credit scores.

Business Impact

Here is how we implemented the plan we had for the company. We created this system in 4 steps –
  • Data collection from mobile devices: We fetched the information from the mobile SMS data of the customer using mobile SDK after getting the consent of the customer. After fetching the information, we stored the data on the server database of the system.
  • Data Warehousing: After collecting data, we then stored the data into a common data warehouse that was built for the system. After the successful warehousing, the data then gets transformed in the way so that the NLP engine can pass it to the credit scoring engine.
  • NLP engine implementation to fetch the information from the data: This is the engine designed to read the raw SMS data being collected from the customer’s mobile devices and fetching the desired information like expenses, income, investments, and more depicting the financial strength of the customer.
  • Credit score engine installation: After this, the information including the financial strength and the type of device, contact list, and more are fed to the ML-based credit score engine. At first, this system was created on a rule-based algorithm in which the data gets weighed and is tagged to different data points. Once we had enough data points, the algorithm was upgraded and was trained to calculate credit scores automatically.

Conclusion

As per the requirement of the client, they needed a system to calculate the credit score on the basis of a more reliable data source than Credit Bureau Data. For this, we designed an efficient system that collected the data from customers’ mobile devices and calculated the credit score on the basis of that. This entire process helped them speed up the process of client conversion.

You saw how we solved their business problem. We can do the same for you.

Submit your details & our experts will reply you with a quote very soon.

Your information is kept secure and confidential. We respect your privacy and will not share your data with third parties without your consent.

Frequently Asked Questions(FAQs)

How do I know if a solution like this is right for my business?

A solution like this is a good fit if your teams spend more time collecting and reconciling data than acting on it, if reports from different departments do not match, or if decisions depend on delayed or manual reporting. It also fits when your data is growing in volume or sources, and your current setup struggles to keep up.

DataToBiz starts with a discovery call to review your goals, data sources, and reporting gaps. You get a clear view of the expected outcomes, effort, and timeline before any commitment.

Can DataToBiz customize the solution for my industry and current data setup?

Yes. Every solution DataToBiz delivers is configured around your industry, business processes, and existing data environment. The team assesses your current systems, data quality, and reporting needs first, then designs a solution that works with what you already have instead of replacing it. This applies across industries such as manufacturing, retail, healthcare, finance, and logistics, and across setups ranging from legacy databases to cloud data platforms.

Which tools, platforms, and data sources does DataToBiz work with?

DataToBiz works across the major analytics, cloud, and data engineering stacks. For BI and visualization, this includes Power BI, Tableau, and Looker. For cloud platforms, it includes Microsoft Azure, AWS, and Google Cloud. For data warehousing and processing, it includes Snowflake, Databricks, and SQL-based databases. Data sources include ERP and CRM systems, spreadsheets, APIs, IoT and sensor data, and third-party applications.

If your stack includes a tool not listed here, the team can assess compatibility during discovery.

How long does a project like this take with DataToBiz?

Most projects with DataToBiz take from a few weeks to a few months. The timeline depends on how ready your data infrastructure is for the initiative and how many systems need to connect. Smaller solutions go live quickly, while larger programs roll out in phases so you see value early. After an initial discovery call, you receive a timeline with clear milestones.

How does DataToBiz keep my data secure and compliant during the entire engagement?

Security and governance is part of the plan from the first conversation. DataToBiz works within your security policies and follows the regulations that apply to your industry. The project usually Access stays limited to the people who need it, and confidentiality terms cover the engagement from kickoff to handover.

What support and results can I expect after the solution goes live?

The work does not stop at go-live. Your team gets a proper handover and guidance on using the solution, and DataToBiz stays available for questions and improvements. The business impact of the project is measured against the goals set at the start, so you can see exactly what changed for your company.

DMCA.com Protection Status