Machine learning in banking is gaining popularity in the FinTech sector, from public relations to investment decisions. But how exactly can tech companies incorporate this technology in finance to drive real results? In this article, Intellias lays out machine learning use cases in finance.
The profitable alliance of machine learning and finance
Custom machine learning development is used in various aspects of our lives today. It helps us get from point A to point B, suggests what to do with pressing issues, and is getting better at holding conversations. No wonder in the world of finance we keep hearing about new machine learning use cases in banking. Applications of artificial intelligence (AI) in FinTech are predicted to be worth up to $7,305.6 million by 2022.
AI and ML are the most impactful trends in the FinTech industry
Source: Mediant – Mediant FinTech Trends Report
Machine learning algorithms used in finance work best for pattern identification. They detect correlations among tons of sequences and events, extracting valuable information that’s camouflaged among vast data sets. Such patterns are often missed or simply can’t physically be detected by humans. The ability of ML to learn and predict enables FinTech providers to recognize new business opportunities and work out coherent strategies.
A schematic view of ML in relation to AI and big data analytics
Five notable uses of machine learning in finance
FinTech companies that are exploring machine learning in banking and finance can expect higher interest from venture funds. Venture Scanner examined funding by AI tech categories and concluded that machine learning platforms and machine learning applications not only led the sector in Q2 2018 funding but dominate the industry in all-time funding.
But what makes banking and finance one of the most-targeted business segments for machine learning? It’s definitely the tremendous volume of data and the nearly infinite size of this segment worldwide. There are many machine learning use cases in finance, including for banking and credit offerings, payments and remittances, asset management, personal finance, and regulatory and compliance services. One of the main benefits of machine learning in banking is volumes of data — including accurate accounting records and other numbers — that have been saved by financial companies for years can now be turned into effective business drivers.
Machine learning in FinTech means more loan approvals with lower risks
Interest in peer-to-peer lending has skyrocketed both on the part of borrowers and investors. Along with P2P lenders, traditional banks are also looking for new mechanisms to improve market share without additional risk. Credit scoring is one of the most useful applications of machine learning in FinTech.
Machine learning use cases in finance give lenders better insights into a borrower’s ability to pay by working with far more data and more complex calculations than conventional models. Machine learning processes more layers of data, and isn’t limited to FICO scores and income data. Such applications of machine learning in finance open alternative data sources to lenders.
Thousands of factors, such as data from social profiles, telecommunications companies, utilities, rent payments, and even health checkup records will now count. Machine learning algorithms compare aggregated data points with those of thousands of other customers to generate an accurate risk score. If a risk score is under the threshold set by the lender, a loan will be approved automatically.
Machine learning algorithms at work for loan automation
Source: Tieto – How machine learning can improve accuracy in credit scoring
What are the benefits of machine learning in banking credit scoring?
- More loan approvals attracts borrowers who were previously overlooked.
- Trustworthy credit scores with fewer lending risks.
Here are several providers worth mentioning in this category:
- ZestFinance works on machine learning-based credit models to generate more profitable underwriting.
- Deserve uses machine learning to provide users with credit cards even if they have no credit score or need to rebuild their credit.
- Intellias has extensive experience in FinTech solutions. They’ve assisted a US-based SaaS lending provider with developing an ML-enabled credit score calculator and microservices software architecture. It runs with the help of ML algorithms and a custom-built AWS-based fault-tolerant database to get the most data about borrowers and their businesses.
Machine learning applications in finance can help businesses outsmart thieves and hackers
A typical fraud detection process
Source: Maruti Techlabs – How Machine Learning Facilitates Fraud Detection
Fraud in the FinTech sector is a knotty problem for all service providers, regardless of their size and number of customers. It’s a well-discussed problem with known complications. And ML might just hold the solution.
Machine learning in FinTech can evaluate enormous data sets of simultaneous transactions in real time. Moreover, the ability to learn from results and update models minimizes human input. Using machine learning techniques, FinTech providers can label historical data as fraudulent or not fraudulent. By running ML algorithms, the system will learn to recognize activity that looks suspicious. ML models can detect unusual activity, for instance in the course of an online transaction.
In 2017, a record-high figure, 78%, of the surveyed organizations were affected by fraud.
What are the benefits of ML for security?
- Fights fraud effectively and effortlessly.
- Prevents the subtlest fraudulent transactions that often can’t be anticipated by manually defined rules.
Comparison of rule-based and machine learning-based fraud detection systems
Source: AltexSoft – Fraud Detection
Here are several providers worth mentioning in this sector:
- Feedzai is a startup that offers one of the most mature machine learning engines, which is quick at taking advanced fraud prevention measures.
- Biocatch combines behavioral biometrics with machine learning to recognize and prevent human and non-human cybersecurity threats mainly in banking, payments, and insurance.
- Ravelin is a London-based company that uses machine learning to prevent and stop fraud in online payments.
Machine learning in banking and finance helps companies comply with ever-changing regulations
The role of machine learning in regulatory compliance
As if billions of dollars spent on regulatory compliance were not enough for financial firms, the majority still have to deal with more new rules and regulations. Focusing on regulatory issues in FinTech and banking requires lots of time and money. Even so, this investment can’t guarantee that all new rules are followed in a timely manner.
Companies are spending an average of $1.34 million on compliance-related technologies in 2017, up from an average of $92,000 in 2011.
Among top machine learning use cases in finance are applications under the category of Regulatory Technology (RegTech). Because ML algorithms can read and learn from a pile of regulatory documents, they can detect correlations between guidelines. Cloud platforms with incorporated machine learning algorithms used in finance can automatically track and monitor regulatory changes as they appear. Banking institutions can also monitor transaction data to identify anomalies automatically. This way, machine learning can ensure that customer transactions comply with regulatory requirements.
What are the benefits of ML in regulatory compliance?
- Banking organizations can more successfully conform with applicable regulations, laws, and supervisory expectations.
- Time-consuming and often tricky tasks can be performed by machines instead of humans.
- Regulatory work can be done faster with minimized risks of non-compliance, reducing multiple manual tasks.
Here are several providers worth mentioning in this category:
- Pendo Systems is a FinTech company that works with unstructured data to streamline the compliance process for their clients.
- Compliance.ai is a Silicon Valley startup that uses adaptive machine learning models in FinTech to automate research and track financial regulatory content and regulatory updates in a single platform.
- ComplyAdvantage is a US-based startup that uses machine learning to accelerate FinTech compliance and enable online fraud prevention tools.
Providers enrich the customer experience using machine learning in customer service
There are several reasons why people choose FinTech services over traditional ones. With machine learning’s ability to delve into petabytes of data to find out exactly what matters to a particular customer, financial institutions can create personalized offers. Even better, machine learning algorithms in banking and finance can analyze customer data and return predictions about a user’s preferences. This way, companies can know what services or offerings a particular client is likely to appreciate.
AI and ML platforms in the framework of customer service infrastructure
Source: CustomerThink – Customer Service beyond the Chatbot Hype
Another example of a rewarding machine learning use cases in banking is a chatbot. Machine learning supports a new generation of chatbots that are more intelligent, human-like, and client-oriented. As chatbots learn from each interaction, the conversations they hold become more helpful and personalized. Less need to build or expand customer service departments is another great benefit, especially for small and mid-sized financial operators.
Chatbots will be behind 85% of all the customer service interactions by the year 2020.
What are the benefits of ML for customer service infrastructure?
- Increased revenue thanks to improved user experiences and better productivity.
- Companies that use machine learning for advanced customer service are perceived as something more in touch.
- Clients appreciate innovation-led FinTech businesses that simplify their lives and add real value.
Here are several providers worth mentioning:
- Kasisto uses AI and ML algorithms to power omnichannel virtual assistants.
- Wells Fargo was the first US bank to launch an AI-driven customer chat experience for Facebook Messenger.
- Bank of America’s Erica, an AI-based virtual assistant, was launched in March 2018 and helped more than 1 million users in the first three months.
Machine learning is the new superpower on the stock market
How is machine learning used in finance future telling? The vast volumes of trading operations result in tons of historical data — an unlimited potential for learning. Still, historical data is only the grounds on which predictions are made. ML algorithms monitor data sources available in real time, such as news and trade results, to pinpoint patterns indicating stock market dynamics. The task left to traders is to determine which ML algorithms to include in their strategies, make a trading forecast, and choose a behavioral pattern.
A typical workflow for a trading system using supervised learning
Source: A Machine Learning Framework for Algorithmic Trading on Energy Markets
What are the benefits of ML in stock market?
- The predictive capacities of machines are unlimited, unlike those of a human.
- Machine learning can detect the slightest indicators of prices going up or down.
- Machine learning can easily compare data over several decades.
- Machine learning algorithms can make trading decisions extremely quickly.
- No bias from human interpretation.
Here are several providers worth mentioning in this sector:
- Sentient Technologies is an AI company that’s developing and applying proprietary quantitative trading and investment strategies using distributed artificial intelligence systems.
- Walnut Algorithms is a European startup that offers AI and ML finance solutions for investment management.
- I Know First is an Israeli company offering stock forecasts based on predictions of machine learning algorithms.
Final thoughts
The world of financial services has entered the era of artificial intelligence and machine learning. The number of uses of machine learning in finance is constantly rising. The technology is beginning to play a significant role in various processes, including loan approvals, stock forecasts, and fraud prevention. Yet not many FinTech providers have embraced machine learning as a critical driver for financial services. More accessible machine learning tools, a variety of algorithms, and decent computing capacity will only increase the number of interactions between machine learning and custom software product development in FinTech, so it’s high time to catch up with this trend.
Contact experts at Intellias if you plan to develop or scale digital solutions based on recent developments in machine learning.
