How This Delhi-Based Startup Uses AI To Streamline Legal Workflows Of Fintech Firms

Emerging technologies like AI and ML allow us to predict the future events based on the available data, automate workflows, and mitigate human errors. Machine learning, predictive analytics, and artificial intelligence have upended the fintech industry with improved credit risk assessment in lending, fraud detection in payments and a host of other applications.

For this week’s startup column, Analytics India Magazine spoke to Anand Agrawal, co-founder & CTO at Credgenics, to understand how the Delhi-based fintech startup is leveraging emerging technologies to assist lenders in streamlining and digitising collections and legal workflows. 

Founded in 2018 by IIT-Delhi Alumni and lawyers Rishabh Goel, Anand Agrawal and Mayank Khera, Credgenics is a SaaS platform offering on-demand dispute resolution and mediation services.

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Flagship Products

At Credgenics, Automated Communication and Legal Notice Module are the two flagship products for the lenders. Automated communication helps in designing the digital journey by defining a rule to segment the borrowers and set communication templates. The legal module simplifies the entire journey of issuing a legal notice to the borrowers, sending a soft copy via digital channels (SMS, email, and WhatsApp) and physical modes (via courier partners).

Credgenics allows users to manage all collections and repayments on a single platform. The process entails uploading the data on the platform, generating actions using automated rule defining widget, issuing notices, and then approaching borrowers using any of the five modules such as cloud-based calling, automated communication, field executive android app for on-field collection, legal notice and litigation workflows.

Currently, the company is working with 32 NBFCs/Fintechs and 7 banks including Kotak Mahindra Bank, HDFC, ICICI Bank, Clix Capital, Shubh Loans, LoanTap, Udaan, etc., helping them streamline their recovery section with a blend of data-driven technology and legal solutions.

What’s The Differentiator?

Agrawal said, “Our mission is to enable automation first, followed by ensuring cost-effectiveness and then create a one-stop solution to minimise bad debt. The ‘plug and play’ SaaS solution digitises the entire collections process on an easy-to-use interface and provides an AI-powered personalised collections strategy, which optimises and automates action through SMSs, call centres, field agencies, and legal notices.”

He added, “The uniqueness of the product is around the lawyer-led mediation services which result in better resolution efficiency, quicker collection, and a more empathetic experience for the borrower. We believed this could potentially reimagine a more sustainable and ‘borrower centric’ paradigm in the post-default recovery process, which further cemented our conviction.”

Use of AI/ ML @ Credgenics

The debt resolution platform provides actionable delinquent credit analytics and collection assessment insights to the financial firms. Agrawal said the lenders can take collections completely in-house with minimum human intervention by applying their flagship AI/ML tools for recovery chance predictor.

Core Tech Stack

Credgenics has a microservice-based architecture deployed over Kubernetes to enable auto-scaling. The main components:-

  • Front end: Reactjs, Native Android
  • Back end: Postgres, Redis, SQS
  • Code: Python 3.7
  • Infra: AWS, EKS, Sonarqube


The firm has raised INR 27 Crore ($3.5 million) in a pre-Series A round led by Accel Partners, DMI Alternatives fund, with participation from existing investors Titan Capital. 


Credgenics look for proactive candidates with a can-do attitude and analytical mindset. The company also sees if their values are aligned with the culture and core values of Credgenics.

Future Roadmap

“Our long term vision is to become the world’s most convenient, secure, and cost-effective debt collections platform. We have been working with lenders to bring more product features like Skip-Tracing tool, advanced intelligent layer around borrower persona, and early warning signals. Our primary focus will be a data-first approach to understand the borrower persona based on loan and applicant details provided and mix with collection data to drive meaningful insights helping make the collections much more efficient,” said Agrawal.

Ambika Choudhury
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.

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