Shriram One App: A Key Channel for New Business Acquisition

Shriram Finance has developed a SuperApp (Shriram One App) offering all the financial services from the group in a single application. How much business have you been able to do in FY 24-25 and Q1 of FY26 from the app? Have the transactions been done completely using the app or partly on the app and partly offline?

The Shriram One App has quickly evolved into a key digital channel for both servicing and new business. With 1.9 crore downloads and 1.4 crore active users, the app today drives a significant portion of lead generation, customer onboarding, UPI transactions, and bill payments.

From a business perspective, the app functions in a hybrid model—most customers initiate their journey digitally, while fulfillment often happens through our extensive branch network. This ensures that even semi-formal MSMEs and Tier 3+ customers, who may be more comfortable with assisted journeys, are seamlessly integrated into our ecosystem.

In FY24–25 and continuing into Q1 FY26, we’ve seen the Shriram One App becoming not just a service platform, but also a customer acquisition engine, with nearly 60% of new-to-group customers now coming digitally first. Transactions like loan servicing, EMI payments, and utility payments are being done completely within the app, while loan disbursements often remain a digital-physical mix.

What is the role of fintech startups in digitalisation at the group? How much work have you done in-house? What have been the outcomes?

Fintech partnerships have been catalytic in accelerating our innovation journey. Collaborations with nimble fintechs bring agility, new-age customer engagement models, and alternative data sources for risk assessment.

At the same time, a large part of the core architecture is built in-house—for instance, our LOS (Ziva), lead management systems, and the integration layers that connect digital journeys to our branch infrastructure. Our LOS has been transformed into a fully digital, end-to-end platform that enables instant eligibility checks, eKYC-based validations, and digital loan agreements in minutes. With embedded credit bureau access, pre-scoring models, and smart calculators, it powers rapid approvals, personalized offers, and seamless handoffs between digital and branch channels. This in-house control ensures data security, operational efficiency, and a scalable experience even in Tier 3+ markets.

Outcomes:

• Faster deployment of customer-facing features like instant eligibility checks and referral programs.

• Deeper personalization through AI/ML-driven segmentation.

• Higher adoption in Tier 3+ markets by leveraging vernacular UX and assisted journeys.

By developing core capabilities internally and incorporating fintech innovations when advantageous, we achieve both control and swiftness.

Which are the various Digital Public Infrastructure (DPI) platforms you are using to enhance swift loan processing, keeping asset quality in mind? What have been the outcomes?

We are deeply aligned with India’s Digital Public Infrastructure (DPI) stack, particularly for swift loan processing and maintaining asset quality. Key integrations include:

• Aadhaar-enabled e-KYC for instant identity verification.

• e-Sign and Digital Agreements for paperless disbursals.

• UPI and Bharat Bill Pay for seamless collections and repayments.

Outcomes: What earlier took 24–48 hours now gets completed in 20–25 minutes. This acceleration reduces paperwork, strengthens compliance, and enhances the customer’s first experience with us—while giving us real-time data to safeguard asset quality.

How are you leveraging AI-powered underwriting models to accelerate loan approvals and reduce manual intervention?

At Shriram Finance, we’re using AI and ML in focused ways to strengthen both efficiency and customer engagement. These models support customer segmentation, behavioral analysis, and delinquency prediction. For instance, they help us understand whether a customer is more responsive to SMS, WhatsApp, or email, which makes our communication sharper. We also rely on repayment patterns and customer activity data to train our models for segmentation, cross-sell opportunities, and delinquency prediction. Predictive models further enable us to personalize marketing communications based on customer behavior. Together, these applications are helping us improve turnaround times and decision-making.