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.Why large financial institutions are interested in this technology is the same reason they are interested in anything: machine learning, properly applied, can significantly improve the bottom line. In fact, there is a manifold advantage for companies that embrace machine learning technology, both in terms of replacing legacy systems and when developing enterprise or custom solutions. With the ability of machine-learning-based applications to catch costly errors, to improve efficiency, to augment decision-making processes, and to improve the customer experience, machine learning offers benefits on the front end, the back end, and everywhere in the middle. But it all comes at a cost, of course.
There are various applications of machine learning used by the FinTech companies falling under different subcategories:
Financial service providers have no greater responsibility than protecting their clients against fraudulent activity. Financial fraud costs Americans, alone, 50 billion dollars annually. Old ways of keeping clients’ accounts secure are no longer good enough. With every advancement in data security, criminals have stepped up to the challenge. To protect clients’ data against increasingly sophisticated threats, institutions and companies must stay one step ahead of hackers. Machine learning enables applications to thwart security breaches by out-thinking the criminals.
By comparing each transaction against account history, machine learning algorithms are able to assess the likelihood of transaction being fraudulent. Unusual activities, such as out-of-state purchases or large cash withdrawals, raise flags that can cause the system to introduce steps to delay the transaction until a human can make a decision. In many cases, depending on the nature of the attempted transaction, a purchase or withdrawal attempt may be automatically declined by the system.
It should come as no surprise that machine learning technology can be a powerful ally in the quest for better risk management. While traditional software applications predict creditworthiness based on static information from loan applications and financial reports, machine learning technology can go further and also analyze the applicant’s financial status as it may be modified by current market trends and even relevant news items.
By applying predictive analysis to huge amounts of data in real time, machine learning technology can detect rogue investors working in unison across multiple accounts — something that would be nearly impossible for a human investment manager.
Computer aided trading services have been around for some time. They allow investors to have an order placed when a stock reaches a predetermined price, and to sell when that price drops below a certain limit. By automating functions, such platforms make trading easier for large and small investor, alike. While they can even make recommendations based on automated analysis of market trends, they have limitations.
In recent years, hedge funds have increasingly moved away from traditional predictive analysis methods and have adopted machine learning algorithms for predicting fund trends. Using machine learning, fund managers hope to identify market changes earlier than is possible with traditional investment models.
Poor customer service remains one of the chief complaints among consumers, regardless of the industry. Originally, the complaints centered on slow customer service, but with the universal utilization of automated phone support, customers are frustrated by not being able to speak to a human. For the innovative financial service company willing to invest in machine learning technology, this should not represent a problem so much as an opportunity.
The advantages of automated support systems include directing the customer to the correct department, giving them the option to resolve minor problems by using the automated interface, and keeping the customer from having to wait for someone to answer the phone — all without human interaction. The company benefits by not paying salaries to personnel who would handle these tasks, and the customer (supposedly) benefits by having their problem handled with the expeditiousness of modern computers.
Having read of the many ways in which machine learning can keep accounts secure, improve risk management, and offer investment strategies, you might not expect the technology to also be a good marketing tool. The ability to make predictions based on past behaviors is fundamental to any successful marketing effort. By analyzing web activity, mobile app usage, response to previous ad campaigns, machine learning software can predict the effectiveness of a marketing strategy for a given customer.
With the online marketing power of Google, now augmented by machine learning, it is possible for developers working in the financial sector to create smart tools that make the job of marketing executives easier than ever.
Among the top considerations for any network administrator or data security professional is how to recognize suspicious patterns occurring across their networks. The challenge to identify such patterns lends itself perfectly to the capabilities of machine learning. The power of intelligent pattern analysis, combined with big data capabilities, certainly gives machine learning technology an edge over traditional, non-AI tools. One might go so far as to declare machine learning as the last hope of securing critical networks against professional and state-sponsored cyber attacks.
Companies using machine learning are presented here:
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.
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.
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 is expertise in big data mining, machine learning algorithms, security and consumer Web UX.
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.
Bloomberg: Bloomberg connects decision makers to a dynamic network of information, people and ideas; it quickly and accurately delivers business and financial information, news and insight around the world. Bloomberg develops state-of-the-art solutions for the financial community by leveraging methods from statistics, natural language processing and machine learning.
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.
FinGenius: FinGenius is a bank-grade platform that combines artificial intelligence, machine learning, natural language processing and human-like reasoning to simplify interactions with complex data for banks and insurance companies.
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.
Binatix: Binatix is a learning trading firm, possibly the first to use the state-of-the-art machine learning algorithms to spot patterns that offer an edge in investing.
Brighterion: Brighterion offers the world’s deepest and broadest portfolio of artificial intelligence and machine learning technologies which provides real-time intelligence that matters from all data sources, regardless of type, complexity and volume.
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.
Whether a financial service provider purchases a solution among those increasingly-available on the market, or invests in custom development, the current cost of implementing any AI platform must be considered—not only in terms of software, but, in many case, hardware cost, also. However, as with any good investment, the payoff far outways the initial expense, as costs are reduced and profits are realized over time. For some applications such as fraud prevention, the payoff can be more immediate.