Integrated Financial Ecosystems: AI-Driven Innovations in Taxation, Insurance, Mortgage Analytics, and Community Investment Through Cloud, Big Data, and Advanced Data Engineering

Abstract

Finance has a broad impact on individuals and economies. This is especially true during critical times such as the current pandemic, when unpredictability and policy changes affect an individual’s earnings, savings, investments, and more. Meanwhile, there is a variety of opaque and challenging to understand new financial offers by FinTech companies. Regulatory and technological aspects both call for innovation in this finance. To bring transparency, standard human-understandable explanations and verifications are proposed for consumer finance. Laying the groundwork for citizen sense-making tools that could be part of e.g. FinTech regulation compliance. Task is positioned at the nexus of Financial Machine Learning, Visual Analytics, and Artificial Intelligence (AI) fairness. A new finance-centered story-guided system design pattern illustrated with prototypes. A continuum of visual explanations across a spectrum of reviewing the model’s understanding- and proposing simple Event Glossary on consumer finance—relevant machine-learning explainability (XAI). The goal is to narrate a machine-learned model’s decisions in a human-understandable way, ideally in natural language. This anticipation of potentially askable questions was introduced in the context of ImageNet object recognition, but can be translated to a variety of different tasks, including consumer loans risk for personal finances or reviewing loans applications for a bank. In the personal finance task, for instance, this explanation might answer questions such as “Why did my mortgage rate increase?,” “Why can I afford less of a loan?,” or “Why am I shown this offer?” At the intersection of Visual Analytics and AI fairness, the focus is on explaining text- and entity-based models rather than the previously considered deep image- or speech-recognition ones. Further, the focus on text and event data interpretation, common in financial regulation.