Directory of AI personalisation and customer engagement vendors for banks

FinGoal enriches raw transaction data for community banks, credit unions and digital banking platforms, cleaning and categorising spending into more than 750 categories, deriving over 750 account holder insights, and building behavioural personas and next best actions for every account holder including a view of their off-bank accounts. Its argument is that institutions sit on a goldmine of transaction data most cannot convert into actionable insight, and that clean, categorised and enriched data is the first step before any further personalisation capability can be added. Rather than selling a destination application it embeds as the enrichment layer inside other providers' platforms, with named partnerships spanning customer data platforms, marketing automation, community bank analytics, open banking interfaces, account aggregation and enterprise data warehousing, so institutions can move from account holder data to targeted outreach without custom development.

Fivvy sells customer intelligence to banks and credit unions, turning the transactional and behavioural data an institution already holds into personalised digital experiences and revenue opportunities. The platform analyses mobile application usage, device metadata and transaction history to build a full picture of a customer's spending, saving and investment behaviour, then surfaces contextual sales opportunities in real time, identifies cross selling openings and predicts churn. Institutions are stated to become data driven within three months of implementation. Its own positioning emphasises ethical data collection and privacy compliance without invasive tracking, and its stack is combined with credit bureau data from a named provider.

Flybits is a context aware personalisation platform for banks, credit unions and card issuers, delivering content, products and offers into digital channels based on what each customer needs at a given moment rather than by broadcast campaign. Its 2025 agentic banking capability organises journeys around major life events rather than product lines, with agents unifying cards, loans, deposits and insurance into a single experience such as financing and running a car, which the founder distinguishes explicitly from chatbots and rewards apps. The platform emphasises privacy preservation and zero party data, offers pre-packaged retail banking and cardholder lifecycle experiences, and is built for delivery with minimal technology involvement under a connect once, deploy widely approach. A research arm partners with academic institutions on future banking experiences. Investors include a card network, two major bank venture arms and established venture funds.

Otomo sells what it calls self driving finance as a service to retail financial institutions, credit unions, fintechs and consumer brands, embedding an automated money management experience inside the institution's own app through a software development kit, an interface and modules for mobile and web, or delivering it white labelled. The consumer sets financial outcomes such as building a rainy day fund, saving for a first home or paying down debt, and the platform then acts on income as it arrives according to those rules, while a curated feed anticipates what the person is likely to want next and an ongoing balance tells them what is safe to spend. Alongside the automation it presents offers and discounts from third parties, which is how the institution earns a share of non interest income from the arrangement. The company began as a direct to consumer application and moved to selling through institutions after interest from that side, and its stated positioning is that a bank should anticipate a member's needs the way a streaming service anticipates taste rather than pushing products at them.

Personetics turns bank transaction data into personalised, proactive customer engagement, serving hundreds of institutions across 30 markets and reaching over 150 million active monthly banking customers. Its multi-stage process enriches raw transactions, cleaning merchant names and adding logos, then runs behavioural and predictive models to read income stability, spending patterns and upcoming obligations, surfacing needs such as a predicted cash flow shortfall so the bank can respond with something timely rather than generic. No-code tools let business teams build and manage contextual insights themselves. Open banking integration extends the picture to a customer's relationships elsewhere. Its argument is that commoditised products and uniform pricing have removed differentiation, so the question is no longer what banks offer but how they engage, and that digital banking has lost the personal understanding branch managers once held.

Pocketnest licenses a white labelled financial wellness platform to credit unions, banks, investment advisers, insurers, benefit providers and employers, coaching their members and staff through ten themes of personal finance in about three minutes a week. The method is built on psychology, behavioural science and coaching rather than budgeting tools, producing actionable tasks and recommendations tailored to each user, with an AI feature delivering the personalisation. It deploys inside an institution's own mobile banking application or as a standalone app under the institution's brand, and the institution's own financial products are placed directly into the coaching flow. The stated value to those institutions is personal, behavioural and financial data about their consumers, enabling wider service, cross sell identification and product distribution.

Psympl sells psychographic targeting to banks, credit unions, wealth managers, insurers and financial technology platforms, working from why people make financial decisions rather than who they are. A foundational one to one consumer survey establishes segments describing how people think about money, risk and decisions, a scoring model then assigns those segments to consumers at national scale through a credit bureau data integration, and the platform generates motivation aligned marketing copy automatically across channels before measuring how motivation affects outcomes by segment and market. It is sold as an enterprise layer that plugs into an institution's existing customer relationship and marketing stack, and reaches community banks and credit unions through a specialist marketing agency channel partner.

Revio Insight reads a community bank or credit union's own core transaction data to show where its customers are banking elsewhere, which competitor products they already hold, and where deposits are quietly leaving. Machine learning models turn that into prioritised revenue and deposit growth opportunities and next best product recommendations across retail and commercial deposits, loans, cards, insurance, wealth, treasury management and merchant services, segmented so marketers, lenders and relationship managers can act on specific customers rather than run untargeted campaigns. The platform is interface driven, works against an institution's existing systems and is positioned explicitly against reporting dashboards, on the argument that most institutions capture only about half of their customers' financial lives.

Serene applies behavioural science to transactional data so banks, lenders and fintechs can detect financial vulnerability before it becomes arrears, harm or a complaint, which is the point at which firms usually notice. Three products run in sequence: one segments customers against the regulator's own drivers of vulnerability and surfaces early signals across health, life events, resilience and capability, a second forecasts how a customer's circumstances are likely to move so support can be timed rather than reactive, and a third recommends specific interventions from tailored messaging to proactive outreach. Output is designed to be auditable so firms can evidence outcomes under the UK Consumer Duty. The company describes itself as infrastructure for financial care and counts a major UK bank as both investor and user.