# NLP in FinTech Applications: Past, Present and Future

arXiv: https://arxiv.org/abs/2005.01320 · HTML: https://mlsanity.com/paper/2005.01320/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 4 of 4)

- [Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Models](https://mlsanity.com/paper/2407.03689/) (2024)
- [Multimodal Document Analytics for Banking Process Automation](https://mlsanity.com/paper/2307.11845/) (2023)
- [FinTech for Social Good: A Research Agenda from NLP Perspective](https://mlsanity.com/paper/2211.06431/) (2022)
- [DSC-IITISM at FinCausal 2021: Combining POS tagging with Attention-based Contextual Representations for Identifying Causal Relationships in Financial Documents](https://mlsanity.com/paper/2111.00490/) (2021)

## Abstract (arXiv)

Financial Technology (FinTech) is one of the worldwide rapidly-rising topics in the past five years according to the statistics of FinTech from Google Trends. In this position paper, we focus on the researches applying natural language processing (NLP) technologies in the finance domain. Our goal is to indicate the position we are now and provide the blueprint for future researches. We go through the application scenarios from three aspects including Know Your Customer (KYC), Know Your Product (KYP), and Satisfy Your Customer (SYC). Both formal documents and informal textual data are analyzed to understand corporate customers and personal customers. Furthermore, we talk over how to dynamically update the features of products from the prospect and the risk points of view. Finally, we discuss satisfying the customers in both B2C and C2C business models. After summarizing the past and the recent challenges, we highlight several promising future research directions in the trend of FinTech and the open finance tendency.