AI for Spend Intelligence & Financial Wellness
A bank or wallet that can explain a customer’s money back to them earns a different kind of loyalty. Eurisko builds the spend intelligence and financial wellness use cases behind that experience: transaction categorization and merchant enrichment, a natural-language spend analyst, cash-flow forecasting with shortfall warnings, subscription intelligence, savings coaching and wellness scoring, for banks, fintechs and wallets across the GCC, the wider Middle East and Lebanon.
Every feature is grounded in the customer’s own transactions, surfaces its confidence instead of guessing, and is engineered for the Arabic and English merchant descriptors regional ledgers actually contain.
Clean transactions are the product underneath every money feature.
Personal finance features live or die on data quality. A descriptor like “POS PURCHASE 4521 RYD*AL NAKHEEL” has to become a merchant, a category and a location before any insight, budget or nudge can be trusted, and in this region the descriptor may be in Arabic, transliterated Arabic or both.
Eurisko builds the foundation first: entity resolution and categorization models trained on regional merchant data, with confidence scores and an exception queue so low-confidence items are reviewed rather than silently misfiled. The money assistant, forecasts and coaching sit on top of that foundation and inherit its accuracy.
The result is a set of features customers return to daily, inside the mobile banking or wallet app you already run, with the categorization taxonomy and the tone of voice your brand and your regulator expect.
Eight ways to turn a ledger into financial wellness.
From the data layer to the coaching layer, each use case is scoped to be proven on a masked transaction sample and then shipped as a feature inside your app.
Intelligent spend categorization
Classifies every transaction into customer-friendly categories and subcategories with a hybrid of machine learning, merchant knowledge and rules. Low-confidence items are surfaced for review rather than misfiled.
Merchant name normalization & enrichment
Resolves noisy Arabic and English descriptors to a clean merchant or brand, then enriches the record with category, location and confidence: the foundation for budgets, offers and analytics.
Natural-language spend analyst
A conversational layer over categorized transactions, balances and budgets. Questions in Arabic or English become governed queries with a visual answer and an explanation, never invented numbers.
Cash-flow forecast & shortfall warning
Learns recurring inflows and outflows, forecasts the balance ahead and flags the dates where obligations may exceed funds, with scenarios for a late salary or an unexpected bill.
Subscription & recurring payment intelligence
Groups repeated charges into recurring commitments, predicts the next charge and detects material price changes or inactivity, so customers see what they pay for and what they could cancel.
Personalized savings & budget coach
Builds a realistic savings plan from income, essentials, goals and constraints, then turns it into small explainable actions and shows how each one moves the target date.
Financial wellness score & nudges
Scores cash-flow stability, savings buffer, bill regularity and spending volatility transparently, and attaches a practical, non-judgmental nudge to every component.
Responsible & sustainable spend insights
Maps categorized spend to published emission factors and returns indicative ranges by category, with confidence bands, a transparent methodology and optional behaviour suggestions.
Ask a question, get a grounded answer.
The money assistant is the use case customers notice first, so it is a useful illustration of the engineering underneath. A question such as “how much did I spend on dining this Ramadan compared to last year?” is parsed into a governed query against a semantic layer of approved metrics, executed on the customer’s own categorized data, and answered with a chart and a plain-language explanation of the drivers.
The model never computes the number itself and cannot invent one: it chooses the query, the chart and the words. Guardrails keep the conversation inside the customer’s data and the bank’s approved scope, and hand over to a person or a help article when a question falls outside it.
The same semantic layer feeds the forecasts, the subscription inventory and the coaching actions, so every feature agrees with every other feature and with the statement.
Built for open banking, salary cycles and two scripts.
Open banking is making spend intelligence a competitive necessity in the Gulf: Saudi Arabia’s open banking framework under SAMA and the UAE’s open finance regulation under the Central Bank of the UAE let customers share accounts across providers, and the provider with the clearest picture of their money wins the relationship. Categorization, enrichment and forecasting are the features that picture is made of.
Regional cash flows have their own shape: monthly salary credits through wage protection systems, school fees and rent paid in large instalments, remittances to and from family abroad, and in Lebanon, multi-currency accounts and cash-heavy patterns. Our recurring-payment detection and forecasts are trained and evaluated on these patterns, and the Arabic merchant universe is handled as a first-class problem rather than an edge case.
A process built on evidence.
Discovery & success metrics
A workshop with the business owner, the data owner and the technology lead fixes the decision to improve and the number that proves it.
Data readiness
We profile the sample, masked or synthetic, and say plainly what it can and cannot support before anything is built.
Prototype
Model, prompt, retrieval or optimization logic plus a working demo your team can click through, not a slide.
Evaluate & play back
Accuracy, groundedness, fairness and cost are measured against an agreed test set and reported with their limitations.
Productionize
Integration, security review, MLOps and monitoring inside your perimeter, scoped only once the evidence supports it.
Frequently asked questions.
How accurate is transaction categorization on Arabic descriptors?
Accuracy depends on the merchant universe, which is why we measure it on your descriptors rather than quote a generic number. The hybrid approach, machine learning plus merchant master data plus rules, reaches audit-ready accuracy on the bulk of volume, and every low-confidence item is routed to a review queue instead of being guessed.
Can the money assistant give financial advice?
It is designed not to. It explains the customer’s own data, suggests actions from an approved library and declines or hands over when a question moves into advice, in line with the consumer-protection expectations of regulators such as SAMA and the Central Bank of the UAE.
Do we need open banking data to start?
No. Your own transaction ledger is enough to prove categorization, forecasting and coaching. Open banking data, where available, widens the picture to accounts held elsewhere, and the same models apply.
How does this fit our existing mobile banking app?
As features inside it. Eurisko builds mobile banking channels and the Eurisko DXP platform, so the spend layer is delivered as APIs and screens that plug into your app, not a separate product customers have to find.
Does our data have to leave the country or our data centre?
No. For banks, telecoms and government entities in Saudi Arabia, the UAE, Kuwait, Oman and Lebanon we deploy inside your private cloud or on-premises, use open-weight models when data cannot leave the perimeter, and start with masked or synthetic samples so no customer record is needed to prove the idea.
Where do we start if we want more than one use case?
Pick a small portfolio rather than isolated ideas: one quick win, one strategic differentiator and one operational efficiency case, ideally sharing one data foundation and one evaluation framework. We run a short selection workshop with your business, data and technology leads to shortlist by value and data readiness.
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