Applications of Machine Learning in FinTech

February 5, 2021

Machine learning is a kind of AI that provides computers with the ability to learn without being specifically programmed. The science behind ML is application-oriented. Several startups have disrupted the FinTech ecosystem with ML as their key technology.

There are various applications of ML used by FinTech companies falling under different subcategories. Here are some of the applications of machine learning and companies using such applications.

1. Predictive Analysis for Credit Scores and Bad Loans

Companies in the lending industry are now using machine learning to predict bad loans and build credit risk models.

loan Processing

Here are a few companies using this application:

  • Lending Club: The company is the world’s largest online marketplace which connects investors and borrowers. It uses machine learning for predicting bad loans.
  • Kabbage: Atlanta-based Kabbage, Inc. is an online FinTech and data company. It provides funding to consumers and small businesses through an automated lending platform. Kabbage specializes in building the next-generation machine learning and analytics stack to create credit risk models and analyze the existing portfolio.
  • LendUp: LendUp aims to improve payday lending. The company is opening up its vault to let other organizations offer similar services via its LendUp API. LendUp uses ML and algorithms to find the top 15% most likely to repay their loans. It then charges them interest rates starting from 29% without rollover fees or hidden charges.

2. Accurate Decision-Making

Financial processing and decision-making could be enhanced by machine learning technologies that allow computers to process data and make decisions (such as credit-related) quickly and more efficiently. Some of the companies using such applications are:

  • Affirm: Affirm is a tech- and data-driven finance company. It mines massive amounts of data to successfully rewrite the rules on credit evaluation. The company uses machine learning models o protect against fraud and build credit data.
  • ZestFinance: ZestFinance uses ML techniques and large-scale data analysis to intake vast amounts of data and make more precise credit decisions. The company takes a different approach to underwriting by using ML and data.
  • BillGuard: A personal finance security company that alerts users to bad chargers; it has expertise in big data mining, machine learning algorithms, security, and consumer web UX.

3. Content/Information Extraction

Information extraction has been a major application of machine learning. It involves extraction from Web content like articles, publications, documents, etc. The various companies using these applications are mentioned below:

  • Dataminr: A leading real-time information discovery company, Dataminr transforms real-time data from Twitter and other public sources into actionable signals. It identifies the most relevant information in real time for clients in the finance sector. It trawls social media and other sources using complex ML to identify significant or newsworthy posts and flags them for its clients, that too in real time.
  • AlphaSense: AlphaSense is a financial search engine which solves the fundamental problems of information abundance and fragmentation for knowledge professionals. The company leverages proprietary natural language processing and ML algorithms to provide a powerful and highly differentiated product with an intuitive user interface.

4. Fraud Detection and Identity Management

According to IBM research, fraud costs the financial industry approximately $80 billion annually; US credit and debit card issuers alone lost $2.4 billion.

“We are able to apply complicated logic that is outside the realm of human analysis to huge quantities of streaming data.” – Manager, Machine Learning Technologies Group, IBM Research

With the help of ML, fraud detection can be made effective and efficient. The solutions created will be able to analyze historical transaction data to create a model which can detect fraudulent patterns. Some companies also using ML for biometric authentication. Prove’s UnifyID is one of the companies working in this field:

  • UnifyID: UnifyID makes authentication better for users not only in terms of security but also in terms of user experience via the first implicit authentication platform. Its highly innovative approach is able to authenticate users via behavioral and environmental factors with a high degree of accuracy without negatively impacting user experience while respecting user privacy. UnifyID’s mobile behavioral biometrics technology is the only solution on the market that can definitively authenticate a person with a 1:1 match using a person’s behavior and movement. 

5. Building Trading Algorithms

Machine learning is used in creating algorithms for trading decisions. Algorithmic trading, also called high-frequency trading, uses automated systems to identify true signals among the vast amounts of data which capture the underlying stock market dynamics. ML provides powerful tools to find patterns in market trends.

Here are companies using machine learning for building trading algorithms:

  • KFL Capital: The predictions made by the company are the output of algorithms, predictive models, and coding. The company employs machine learning algorithms to identify non-random price patterns in financial data.
  • Binatix: Binatix is a learning trading firm that is possibly the first to use cutting-edge ML algorithms to spot patterns which can offer an edge in investing.

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