The Moneythor software solution is a channel-agnostic data-processing engine with a rich API enabling banks and fintech firms to offer improved functionality and experience to their customers through their digital banking services, such as Web, mobile and chatbots.
The solution is a pure Java EE application designed from the ground-up to be bank infrastructure-friendly and scalable. It is deployed on premises in the financial institutions’ technical infrastructure or in the cloud.
The prime focus of Moneythor is in the delivery of real-time, personalised, contextual and actionable recommendations and insights to customers. Examples of these include personal or business financial management (PFM/BFM) features, budgets, goals, automated savings alerts, predictive forecasts, financial literacy material, cross-selling, card-linked offers (CLO) and more.
The solution is packaged with a library of preconfigured content, insights, notifications and nudges ready to be used and covering a broad set of use cases. Custom ones can also be uniquely crafted by the banks in the solution’s user-friendly back-office tool.
The Moneythor solution delivers a flexible framework to enhance the digital banking experience of customers across all devices with out-of-the-box Web integration and a modern developer-friendly API, fully documented and consistent across its front-end and back-end endpoints.
From unified transaction history across accounts, cards and/or wallets to powerful search capabilities, advanced money management features, self-service personalisation and digital engagement tracking, all capabilities are available through the UX-agnostic API.
Leveraging rules-based expert system capabilities, statistical algorithms and machine learning, the Moneythor engine is used to augment, classify and analyse transaction data in order to generate better insights for the financial institutions and their customers.
It covers the processing of multi-bank (Open Banking / PSD2) and multi-currency instruments, custom events, the auto-categorisation of both income & expenses, the normalisation of transaction details for improved analytics, the automated detection of trends, patterns and more.
