Discovery Bank Shows How AI Could Change App Engagement

A Discovery Bank client does not need to ask their banking app a question to get a financial recommendation. The app is already watching. It reads spending patterns, savings milestones and transaction activity and surfaces personalized next-best actions in real time, Microsoft reported. That continuous loop is what Discovery Bank is aiming to deploy.

Discovery Bank used Azure OpenAI in Foundry Models and Azure Databricks to build Discovery AI, a generative artificial intelligence (AI) system that combines behavioral modeling with real-time client data. The system runs continuously inside the banking app and through WhatsApp, surfacing personalized next-best actions without waiting for customers to ask.

Most banking AI today is a search engine with a conversational interface. A customer asks a question. The system retrieves an answer. The session ends. Discovery Bank’s approach differs in one fundamental way: The AI is always running.

Behind the interface sits a behavioral modeling engine that builds a financial profile for each client, Microsoft noted. That profile draws from spending patterns, savings milestones, engagement history and financial indicators. Generative AI then uses that profile to decide what action to surface next. A client nearing a savings goal gets a nudge. A client with rising discretionary spend gets a forecast. The system does not wait to be asked.

Stuart Emslie, Discovery Bank’s head of actuarial and data science, told Microsoft the team’s competitive advantage lies in combining its behavioral stack with generative AI. The result is a system that surfaces modeled recommendations rather than generic financial tips. Clients gave higher satisfaction ratings to interactions where agents offered next-best actions compared to interactions that addressed only service inquiries.

Latency Was the Engineering Problem

Speed was the barrier between a useful system and a seamless one. Discovery Bank cut response times by more than 50%, bringing interactions from 5-6 seconds down to under 2 seconds on average. That gap matters in a consumer context. A 6-second wait feels like a search engine. A 2-second response feels like a conversation.

The results followed, and client engagement with next-best actions doubled. Seventy percent of clients now engage with personalized recommendations inside the app. Servicing agents process roughly 3,000 questions daily through the system. Traffic through Discovery AI nearly doubled in a single month. Emslie said that the bank expects that growth to continue as more clients engage with the system regularly

A Broader Race for the Financial Relationship

Discovery Bank is not alone in this direction. The question of who owns the AI layer in personal finance is being contested from multiple sides.

ChatGPT now lets Pro users connect financial accounts across more than 12,000 institutions through Plaid and receive answers grounded in their actual spending and cash flow, PYMNTS reported. More than 200 million people already ask ChatGPT questions about personal finance every month.

Perplexity extended its own Plaid integration to let users link bank accounts, credit cards and loans and ask questions drawn from live financial data, PYMNTS reported. More than 75% of Perplexity users already ask finance questions monthly. The platform does not move money. Instead, it positions itself as the insight layer between the consumer and their accounts.

That positioning is what banks are watching. A platform that answers every financial question, grounded in a user’s live account data, is building a relationship that traditionally belonged to the institution. Discovery Bank’s bet is that being inside the banking app, running continuously on behavioral data, keeps that relationship where it started.