What challenges did Shriram Finance face before AI implementation?

Serve customers in a mobile-first ecosystem with one low-friction experience across lending, deposits, insurance, and payments. Personalize recommendations and flag anomalies in near real time. Handle routine customer interactions such as collections and fixed-deposit renewals while reducing branch dependency.

Source

MicrosoftEvidence: Medium50/100

Shriram Finance’s Shriram One app uses Azure-backed AI for personalization and chatbot collections

Use case typeWorkflow automationUpdated Feb 13, 2026

Shriram Finance built Shriram One as a cloud-native super app that brings lending, deposits, insurance, and payments into a single interface for mobile-first customers in India. The platform uses Microsoft Azure-based infrastructure with Microsoft Defender for Cloud and Microsoft Sentinel for security and compliance, while analytics and AI support personalization, anomaly flagging, and routine customer interactions.

Organization: Shriram Finance
Industry: Finance
Location: India
Published: February 2026

Reported outcomes

Monthly fixed deposit volume: More than 275 crore INR

Other quantified impact

App downloads: 20 million

Planned next steps

The source says the organization aims to achieve: Consolidated lending, deposits, insurance, and payments into one app.

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Monthly fixed deposit volume: 275 crore INR increase

Microsoft Source AsiaFeb 13, 2026Customer storyExplicit claimMedium evidence strength

Monthly fixed deposit volumes on the app have grown more than three-fold—from Rs 80 crore to nearly Rs 300 crore in two years.

Normalized claim

App downloads: 20 million increase

Microsoft Source AsiaFeb 13, 2026Customer storyExplicit claimMedium evidence strength

The app has crossed 20 million downloads as of January 31

Normalized claim

Deposit balances: 30% increase

Microsoft Source AsiaFeb 13, 2026Customer storyExplicit claimMedium evidence strength

Personalization models built using AI and machine learning have also helped lift deposit balances by nearly 30 percent.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Shriram Finance
Provider: Microsoft
Maturity: Unknown
Linked source: Microsoft Source Asia

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

1Workflow automation
2Customer service agent
3Real-time analytics

Built Shriram One as a cloud-native platform that consolidates financial products into a single app.
Used Microsoft Azure for workload management and embedded analytics and AI into day-to-day operations.
Added Microsoft Defender for Cloud and Microsoft Sentinel to support security and compliance.
Trained a chatbot to handle routine collections and fixed-deposit renewals and route complex queries to human agents.

Technologies

Microsoft Azure Microsoft Defender for Cloud Microsoft Sentinel Machine learning Chatbot

Monthly fixed deposit volumes on the app grew more than three-fold over two years.
The app crossed 20 million downloads.
Personalization models built using AI and machine learning helped lift deposit balances by nearly 30 percent.
Customers experienced fewer drop-offs and fewer branch visits.

Architecture

A cloud-native super app built on Microsoft Azure with security/compliance services and embedded analytics/AI for personalization, anomaly detection, and chatbot-assisted servicing.

Sources & evidence1

Evidence: Medium50/100Evidence strength