Top agentic AI use cases for Banking in H2 2026

Answer engine visibility for loan, card and deposit products

Borrowers now ask Google AI Overviews, ChatGPT and Perplexity which home loan or card suits them, and fewer click through to the bank’s own page. Pew Research Center found Google users clicked a traditional result on 8% of visits that showed an AI summary, against 15% of visits without one. DRIS tracks how answer engines describe the bank’s products and fixes the source pages they draw on.

the workflow

1. DRIS runs a fixed set of buyer prompts each week, such as best home loan for the self-employed in Pune or lowest forex markup debit card.
2. DRIS records whether the bank is named, which pages are cited and whether the quoted rate, fee or eligibility is current.
3. DRIS traces each wrong or missing answer to the page it came from and drafts fixes: clearer rate tables, FAQ blocks, structured data and updated eligibility text.
4. Human approval: The digital marketing head and compliance approve the page changes and any newly published claims.
5. DRIS publishes the approved changes and re-runs the affected prompts the following week.

Guardrail. Page fixes state rates, charges and eligibility exactly as the product terms and the Key Facts Statement do, with no best or lowest claim the bank cannot substantiate.

Machine-readable rates and fees for AI comparison agents

McKinsey argues that AI agents acting for customers will prioritise the best rates and fees and weaken brand loyalty. If an agent cannot read a bank’s current rates, it judges the bank on someone else’s stale data. MIRA and DRIS keep a structured, current record of rates, fees and eligibility that comparison sites and AI agents can read.

the workflow

1. Treasury or product changes a deposit rate, card fee or loan spread in the rate master.
2. DRIS updates the structured rate tables, schema markup and partner feeds that mirror the rate master.
3. DRIS checks leading comparison sites and aggregator listings for the old figures and drafts correction requests.
4. Human approval: The product owner approves the published feed values and every correction request sent to a third party.
5. DRIS publishes the approved feed and logs each change against the rate master version.
6. DRIS reports which third-party listings still show outdated figures a week later.

Guardrail. Published figures match the rate master exactly, carry their conditions and effective date, and never present an introductory rate as the standard one.

AI-disclosed loan pre-qualification on WhatsApp and web chat

Borrowers increasingly expect to ask questions and check eligibility in a chat rather than a long form. Convexa answers product questions, collects consented eligibility details and hands qualified borrowers to a person or the application journey. RBI’s FREE-AI framework and the Digital Lending Directions make disclosure, consent and a route to a person the baseline.

the workflow

1. A prospect starts a chat about a personal loan or a card on WhatsApp, the website or the app.
2. Convexa says it is an AI assistant, answers from approved product content and asks for consent before collecting any personal data.
3. Convexa runs an indicative eligibility check on the details given and explains that a final decision follows credit assessment.
4. Human approval: The digital lending head approves the conversation scripts, eligibility questions and hand-off rules before launch and at every change.
5. Convexa hands eligible prospects to the application journey or a sales officer with a conversation summary, and offers a person whenever asked.

Guardrail. The assistant never makes a credit decision or quotes a final rate, collects only data the borrower consents to share, and leaves the Key Facts Statement to the lender’s journey before signing.

Deposit retention conversations before fixed deposits mature

When customers, or AI agents acting for them, shop for the best rate, maturing fixed deposits and idle savings balances are the first money to move. CORA spots maturities and large outflows early and prepares a retention conversation the RM or the app can deliver. Liability teams defend balances before they leave rather than after.

the workflow

1. A fixed deposit is three weeks from maturity, or a savings account shows a large transfer out to another bank.
2. CORA reads the relationship from the CRM and core banking export: tenure, products held and past renewal choices.
3. CORA drafts a renewal or retention option within the treasury-approved rate card and a message for the RM or the app.
4. Human approval: The relationship manager approves the offer and message for each customer above the agreed balance threshold.
5. CORA sends approved messages, books RM calls where the customer asks and records the renewal outcome.
6. CORA reports retained and lost balances by segment to the liabilities head each week.

Guardrail. Retention rates come only from the treasury-approved card, and a customer’s decision to move money is respected without repeated contact.

Consent-aware audiences ready for the DPDP Rules

The Digital Personal Data Protection Rules, 2025 were notified in November 2025 and phase in over the following 18 months, so marketing data built on assumed consent now has a deadline. NIA audits every audience the bank uploads to ad platforms and CRM tools and rebuilds them on recorded consent. Growth teams keep reaching customers without carrying lists they cannot defend.

the workflow

1. NIA lists every customer audience in Google Ads, Meta Ads Manager and the marketing automation tool, with its source and upload date.
2. NIA matches each audience to the consent records and the purpose the customer agreed to.
3. NIA drafts a remediation plan setting out which audiences to keep, rebuild on consent or delete, and the consent prompts needed to rebuild them.
4. Human approval: The data protection officer and the marketing head approve the plan and each deletion.
5. NIA carries out the approved changes and runs a monthly check for new uploads without a consent basis.

Guardrail. Personal data enters an audience only for a purpose the customer consented to under the Digital Personal Data Protection Act, 2023, and a withdrawal is honoured on every platform.

Fake loan app and impersonation watch against the RBI directory

Fraudulent lending apps and fake social pages borrow real bank names to reach borrowers. Since July 2025 the RBI has published a directory of digital lending apps as reported by regulated lenders, which gives borrowers and banks a reference point. Echo watches app stores, social platforms and ads for anything using the bank’s name that is not on its own list.

the workflow

1. Echo scans app stores, social platforms, search ads and review sites daily for listings and pages using the bank’s name, logo or product names.
2. Echo compares each find with the bank’s own digital lending apps as reported to the RBI directory and with its official handles.
3. Echo drafts a takedown report for each suspected impersonation with screenshots, links and the platform policy breached.
4. Human approval: The fraud risk or brand protection lead approves each takedown request and any customer warning post.
5. Echo files the approved reports, posts approved warnings and tracks each case to closure.

Guardrail. Echo reports and warns but never contacts suspected fraudsters or the borrowers they targeted, and any case involving customer loss goes to the fraud team.

Partner and LSP co-marketing that names the lender

Fintech partners, DSAs and lending service providers carry a growing share of acquisition, and under the RBI (Digital Lending) Directions, 2025 the regulated lender stays responsible for its LSPs. RUPA produces co-branded creative and landing assets for partners, and the Banking Strategist checks that each one names the lender and states terms correctly. Partnership teams add partners without losing control of what is said in the bank’s name.

the workflow

1. A partner or LSP requests campaign assets for a co-branded personal loan offer.
2. RUPA produces banners, social images and a landing page in the partner’s formats from the approved brand kit.
3. The Banking Strategist checks each asset names the regulated lender, links to the lender’s terms and quotes only approved rates and fees.
4. Human approval: The partnerships manager and compliance approve the asset pack before it goes to the partner.
5. CREA sends the approved pack with usage rules and an expiry date, and spot-checks live partner placements each month.

Guardrail. Every partner asset discloses the regulated lender behind the loan, and partners never alter the approved rates, fees or terms.

First 90 days activation for new accounts and cards

As AI agents make comparing and switching easier, the first months of a relationship decide whether a new account becomes a primary one. LIPI writes onboarding sequences that respond to what each customer has and has not done yet, such as setting up UPI, moving a salary credit or using the card. Retail and cards teams turn openings into active relationships.

the workflow

1. A new savings account, current account or credit card goes live in core banking or the card system.
2. LIPI tracks activation milestones each week: first deposit, UPI or net banking set-up, first card spend and salary credit.
3. LIPI drafts the next message for each customer based on the milestone not yet reached, with a short how-to and one useful feature.
4. Human approval: The onboarding product owner approves the milestone sequences and every message template.
5. LIPI sends approved messages through the app, email and WhatsApp and stops a sequence once the customer is active.
6. LIPI reports activation and early attrition by acquisition channel.

Guardrail. Onboarding messages stay service-led, respect marketing preferences and never push credit to a customer who has not asked for it.

Frequently asked questions