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.

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There are various applications of machine learning used by FinTech companies falling under different subcategories. Let us look at some of the applications of machine learning and companies using such applications.

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1. Predictive Analysis for Credit Scores and Bad Loans

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

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Here are a few companies using this application:

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• Lending Club: Lending Club is the world’s largest online marketplace that 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 to build credit risk models and analyze 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% most likely to repay their loans. Then, it charges them interest rates starting at 29% without hidden charges or rollover fees.

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2. Accurate Decision-Making

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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:

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• 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.

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3. Content/Information Extraction

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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:

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• 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.

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4. Fraud Detection and Identity Management

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

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“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

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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. Prove’s UnifyID is one of the companies working in this field:

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• 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. 

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5. Building Trading Algorithms

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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 massive amounts of data that capture the underlying stock market dynamics. Machine learning provides powerful tools to extract patterns from the seemingly market trends.

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Here are companies using machine learning for building trading algorithms:

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• 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 state-of-the-art machine learning algorithms to spot patterns that offer an edge in investing.

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