{"id":10192,"date":"2026-04-21T15:47:24","date_gmt":"2026-04-21T07:47:24","guid":{"rendered":"https:\/\/www.moneythor.com\/?p=10192"},"modified":"2026-04-21T15:47:24","modified_gmt":"2026-04-21T07:47:24","slug":"comment-la-personnalisation-pilotee-par-lia-transforme-le-secteur-bancaire","status":"publish","type":"post","link":"https:\/\/www.moneythor.com\/fr\/blog\/comment-la-personnalisation-pilotee-par-lia-transforme-le-secteur-bancaire\/","title":{"rendered":"Les banques poss\u00e8dent les donn\u00e9es, mais peinent \u00e0 agir\u00a0: comment la personnalisation pilot\u00e9e par l\u2019IA transforme le secteur bancaire"},"content":{"rendered":"

Banks Have the Data. The Problem is Acting on It<\/span><\/h1>\n

Personalisation has become one of the most discussed priorities in banking,\u00a0<\/span>y<\/span>et for many institutions, it\u00a0remains\u00a0frustratingly out of reach<\/span>\u00a0<\/span>Conversations at a recent\u00a0<\/span>Moneythor<\/span>\u00a0event in Taiwan made this tension\u00a0<\/span>vivid<\/span>. Banks are not short of data, nor are they lacking ambition. The challenge lies in connecting the two in a way that\u00a0<\/span>drives<\/span>\u00a0<\/span>meaningful, real-time customer engagement<\/span>.<\/span>\u202f<\/span>\u00a0<\/span><\/p>\n

A familiar pattern\u00a0<\/span>emerged<\/span>\u00a0across the room.\u00a0<\/span>Banks described complex technology environments shaped by years of incremental change<\/span>\u00a0<\/span>–\u00a0<\/span>new digital layers sitting atop legacy cores, customer data scattered across multiple systems, and no single, unified view of the customer.<\/span>\u00a0Integrating these environments is rarely straightforward, particularly\u00a0when\u00a0<\/span>\u00a0customers\u00a0expect consistency across mobile, online, and every other touchpoint as a baseline.<\/span>\u00a0It is no surprise that so many institutions struggle to build a complete, real-time view of the customer when the underlying data\u00a0<\/span>remains<\/span>\u00a0fragmented.\u00a0<\/span>\u00a0As\u00a0Moneythor\u00a0has\u00a0<\/span>observed<\/span>\u00a0across client deployments spanning multiple geographies, the most persistent integration challenge is not connecting to the bank’s digital channels<\/span>. I<\/span>t is getting quality,\u00a0<\/span>timely<\/span>\u00a0data from back-end systems of\u00a0record into a state where it can\u00a0<\/span>actually be<\/span>\u00a0used.<\/span>\u00a0<\/span><\/p>\n

In some cases, different transaction types<\/span>,\u00a0<\/span>commercial cards, overseas remittances, domestic accounts, foreign currency accounts<\/span>,\u00a0<\/span>live in entirely separate systems, each with its own format, protocol, and latency profile<\/span>. The result is a patchwork of data that is difficult to enrich, harder to act on, and\u00a0<\/span>nearly impossible<\/span>\u00a0to deliver in real time.<\/span>\u00a0<\/span><\/p>\n

The Shift Toward Modular Transformation<\/span><\/span><\/span><\/h2>\n

What is changing<\/span>\u00a0is how banks are choosing to address\u00a0<\/span>this<\/span>\u00a0problem. Rather than pursuing large-scale transformation programmes that\u00a0<\/span>attempt<\/span>\u00a0to replace existing infrastructure, there is a growing preference for<\/span>\u00a0targeted, modular approaches. The\u00a0<\/span>concept<\/span>\u00a0of a Value-Added Function Block (VAFB) is gaining traction<\/span>\u00a0<\/span>–\u00a0<\/span>the idea of enhancing what already exists rather than rebuilding the entire\u00a0<\/span>stack.<\/span>\u00a0<\/span>banks prefer building blocks that integrate into their complex “systems of systems,”\u00a0<\/span>leveraging\u00a0<\/span>existing data to generate new insights, automate tasks,\u00a0<\/span>and deliver<\/span>\u00a0intelligent services. This reflects a broader shift in thinking<\/span>:<\/span>\u00a0institutions\u00a0<\/span>are\u00a0prioritising\u00a0<\/span>speed\u00a0and adaptability, choosing to build differentiation on top of proven, configurable foundations<\/span>.<\/span>\u202f<\/span>\u00a0<\/span><\/p>\n

At its core, this is precisely what the\u00a0Moneythor\u00a0engine is designed to do. It is a high-performance, scalable event-processing and orchestration platform that ingests real-time and batch data from a wide range of sources<\/span>\u00a0<\/span>(<\/span>accounts, cards, digital wallets, and Open Banking feeds<\/span>)<\/span>\u00a0and transforms that raw data into enriched, actionable intelligence. Rather than replacing the bank’s existing infrastructure, it sits alongside it, making the most of what is already there.<\/span><\/p>\n

From Raw Data to a Single Source of Truth<\/span><\/span><\/span><\/h2>\n

Even with the right architectural approach, the conversation quickly returns to data<\/span>\u00a0quality<\/span>. The ambition is not simply to collect more of it<\/span>, but\u00a0<\/span>to make it usable<\/span>.\u00a0<\/span>\u00a0<\/span><\/p>\n

Banks\u00a0at the roundtable\u00a0spoke about the need to unify siloed data sources into something coherent:\u00a0a single source\u00a0of truth\u00a0that allows them to understand customers in context, not\u00a0in fragments. This is where many initiatives stall.\u00a0<\/span>Raw data rarely delivers value on its own.\u00a0<\/span>It needs to be enriched, structured, and continuously updated before it can support meaningful engagement.<\/span>\u202f<\/span>\u00a0<\/span><\/p>\n

The\u00a0Moneythor\u00a0engine addresses this through AI-powered transaction categorisation and enrichment<\/span>,\u00a0<\/span>using machine learning models including multinomial logistic regression and text similarity techniques to automatically classify and contextualise every customer interaction. It also applies time-series forecasting and pattern recognition to predict cashflow,\u00a0<\/span>identify<\/span>\u00a0recurring transactions, and flag upcoming financial events. The result is not just cleaner\u00a0data<\/span>,\u00a0it is data that is ready to act on.<\/span><\/p>\n

F<\/span><\/span><\/span>rom campaigns to real-time customer context<\/span><\/span><\/span><\/h2>\n

The more\u00a0<\/span>compelling<\/span>\u00a0shift lies in what happens next.\u00a0<\/span>I<\/span>nsights alone are not enough. Dashboards and analytics may describe customer behaviour in detail, yet they do little to influence it unless they are connected to action.\u00a0<\/span>\u00a0<\/span><\/p>\n

The focus is moving towards systems that can\u00a0<\/span>anticipate<\/span>\u00a0what a customer might need and recommend, or even\u00a0<\/span>initiate<\/span>, the next best step. This is where AI begins to\u00a0<\/span>demonstrate<\/span>\u00a0tangible value, particularly when predictive models are combined with\u00a0<\/span>prescriptive logic to move from observation to execution.\u00a0<\/span>\u00a0<\/span><\/p>\n

Within\u00a0<\/span>Moneythor\u2019s<\/span>\u00a0platform, this translation from data to action happens in real time<\/span>.<\/span>\u00a0<\/span>Everyday financial activity becomes personalised recommendations, contextual messages, and intelligent interventions delivered\u00a0<\/span>at the moment<\/span>\u00a0they matter most:<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n