Applications of Machine Learning in FinTech

Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. The science behind machine learning is interesting and application-oriented. Many startups have disrupted the FinTech ecosystem with machine learning as their key technology.

There are various applications of machine learning used by the FinTech companies falling under different subcategories. Let us look at 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 using machine learning for predicting bad loans and for building credit risk models.

Here are a few companies using this application:

Lending Club: Lending Club is the world’s largest online marketplace which connects borrowers and investors. They use machine learning for predicting bad loans.

Kabbage: Kabbage, Inc. is an online FinTech and data company based in Atlanta. The company provides funding directly to small businesses and consumers through an automated lending platform. The Kabbage team specializes in building the next-generation machine learning and analytics stack for building credit risk models and analyzing the existing portfolio.

LendUp: LendUp is in the business of improving payday lending. And it’s now opening its vault to let other organizations offer similar services via its API. It uses machine learning and algorithms to pinpoint the top 15% that are most likely to repay their loans. It charges them interest rates starting at 29% without hidden charges or rollover fees.

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) quicker and efficient. Some of the companies using such applications are:

Affirm: Affirm is a technology and data-driven finance company. They mine vast amounts of data to successfully rewrite the rules on how credit is evaluated. To protect against fraud and build credit data, the company uses machine learning models.

ZestFinance: ZestFinance uses machine learning techniques and large-scale data analysis to consume vast amounts of data and make more accurate credit decisions. ZestFinance takes an entirely different approach to underwriting by using machine learning and large-scale big data analysis.

BillGuard: BillGuard is a personal finance security company that alerts users to bad chargers. The company 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: Dataminr is a leading real-time information discovery company. Dataminr transforms real-time data from Twitter and other public sources into actionable signals, identifying the most relevant information in real time for clients in the financial sector. It trawls social media and other information sources using complex machine learning algorithms to identify significant or newsworthy posts and then flags them for its clients in real time.

AlphaSense: AlphaSense is a financial search engine that solves fundamental problems of information abundance and fragmentation for knowledge professionals. It leverages proprietary natural language processing and machine learning 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 machine learning, fraud detection techniques can be made efficient and effective. The solutions created can analyze historical transaction data to build a model that can detect fraudulent patterns. Companies are also using machine learning for biometric authentication. Here are the companies working in this field:

Feedzai: Feedzai uses machine learning and big data science to make commerce safe. Feedzai machine learning models detect fraud up to 30% earlier than traditional methods.

Bionym: Bionym has developed a biometric authentication device using ECG backed with machine learning algorithms.

EyeVerify: EyeVerify software identifies eye prints, the pattern of veins in the whites of eyes, using machine learning technology.

BioCatch: BioCatch, is a leading provider of behavioral biometric, authentication and malware detection solutions for mobile and web applications. Banks and e-commerce sites use BioCatch to significantly reduce friction associated with risky transactions and protect users against cyberthreats such as account takeovers, Man-in-the-Browser (MitB) malware and remote access (RAT) attacks.

5. Building Trading Algorithm

Machine learning is used in creating algorithms for trading decisions. Algorithmic trading, also called high-frequency trading, is the use of automated systems to identify true signals among the massive amounts of data that capture the underlying stock market dynamics. Machine learning provides powerful tools to extract patterns from the seemingly market trends.

Here are companies using machine learning for building trading algorithm:

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 which is possibly the first to use state-of-the-art machine learning algorithms to spot patterns that offer an edge in investing.