AI and Machine Learning Applications In Finance, FinTech, And Banking
Jan 23, 20204 min read
IT Marketing Expert at Ideamotive. Focused on Growth Marketing, Data Analytics and Marketing Automation.
Big data and financial services always have been good friends. But what are currently the best examples of using AI in financial services?
The reason behind the mutual understanding and well-going of big data and the financial industry is, ironically, the legal background. Keeping the data organized, secure and actionable is necessary due to fiscal and compliance reasons. So in fact, the banking and financial sectors have already done a lot of work that is to be (or had to be) when it comes to sales, marketing, or manufacturing.
On the other hand, the industry is strictly controlled and supervised by regulatory institutions, be that a Federal Reserve System or Securities and Exchange Commission. That’s why the industry is often considered slow and petrified while making any innovations in the sector requires months of negotiations and struggling to keep in compliance.
Combining the big data available and processed in the sector with the blocks on innovative solutions resulted in building a large and dynamic fintech industry, where lean and agile players deliver services that traditional banking struggle.
Also, the banking industry is one of the oldest existing, with the oldest still operating banks reaching the times of the Holy Roman Empire. Yes - the Berenberg bank is around since 1590, so it was established before Giordano Bruno was burnt for heresy and Sumo became a professional sporting discipline in Japan.
And way before this particular bank, the banking industry was operational, with the Code of Hammurabi delivering some banking-related regulations among others.
So yes, this industry has some right to be a bit antiquated. And that’s the next reason why the fintech industry grows so dynamic.
How fintech can support banking
Artificial Intelligence in Finance finds multiple applications. Ideas vary from obvious to insane, yet each delivers new ways banks can serve their customers.
Digital private banking
By combining data mining and natural language processing, financial companies can deliver not only a more personalized experience but also better-designed products. In fact, by leveraging the transactional history of the user, the level of prepared recommendations can reach the accuracy of private banking designed for particularly wealthy customers.
When the service is delivered in the form of a chatbot, the user gets not only a tailored offer but also an experience of being served in a natural and convenient way of a conversation.
Transaction search and visualization
Searching for a particular transaction in a transaction history can be a real looking for a needle in a digital haystack - with no magnet around. Employing a chatbot that understands the inquiry done in a natural language.
The technology is not science fiction or a bold extrapolation. Bank of America already uses a chatbot called Erica that serves their client base as a digital banking assistant. The feature was adopted by over a million users in three months' time.
Client profiling and scoring
Delivering a good prediction on the customers’ propensity to pay the debt is crucial for a bank’s business model - attracting a reliable customer with good terms of a loan while maximizing the income from a risky one is banking and loan business’ bread and butter.
By performing a deep-digging within the customers’ transactional history and habits, Machine Learning in finance can deliver a better prediction and more accurate view of the customer.
Also, the technology can be used to boost the underwriting process that is a basic mechanism behind most insurance operations. The application can be seen in Manulife, a Canadian financial service group, that is the first player in the country to use AI in the underwriting service.
Handling the insurance claim is a lengthy and often frustrating process. The user has to provide detailed information on the claim, the insured property (or anything else). Employing a chatbot reduces the need to fill the form and enables the customer to provide information in a natural, conversational way.
An example comes from Lemonade, a New York-based startup that claims to bring a disruption in a traditional way claims are handled in the insurance business by leveraging the power of AI. The company already have risen $180 million since its founding in 2015.
The Natural Language Processing tools can deliver a significant boost in the speed of processing contracts, especially those repetitive and standard ones. Due to the digitalization of legal agreements and a large database of existing examples, the solutions are already operational.
The stock market delivers another environment where algorithms and Artificial Intelligence in Finance shine. A human trader has limited perception and biologically limited response time. Thus even the most brilliant one meets a glass ceiling built by his own biology.
The computer reacts in a matter of milliseconds if not faster. Also, a computer can control all the companies in the index, not only a fraction. Thus, the only limitation of the digital trader was its lack of intelligence. When augmented with artificial intelligence, digital traders are increasing their advantage over biological ones.
Neotic.ai already delivers AI-powered tools for automated trading for multiple brokers.
Augmented research and background scanning
Combining multiple Machine Learning in finance can significantly reduce the effort put into the screening of the company before the transaction or any credit-related decisions. The examples vary depending on the industry, size, geography, and multiple other factors, yet the basics are shared:
Natural Language Processing delivers sentiment analysis and other tools to check the opinions and general feelings of the company. It can also analyze the internal base of contracts or emails in search of things to optimize or redesign.
Image Recognition performed on satellite images can analyze the placement of shops or the routes that are used to transport goods.
By harnessing the power of anomaly detection technology, financial companies can spot fraudulent transactions not only much faster but also in a more accurate way. Frauds are usually based on repetitive patterns. The greatest challenge is the need for constant monitoring of the data flowing through the system. In the financial sector, the amount of data is gargantuan - according to Capgemini and BNP Paribas digital payments are expected to reach a record of $726 billion by 2020.
Employing an ever-vigilant overseer of the transaction can significantly reduce the risk of fraud and generated losses.
Enhancing other business operations
Banking is not only about money and transactions. As every institution, bank, insurer or any other financial company consists of multiple divisions, be that marketing, sales, IT, or maintenance. Every each process multiple operations that can be supported by Artificial Intelligence in banking by optimizing the costs or enhancing the performance.
A great example comes from Google, where Deepmind, a google-owned company focused on AI research, delivered a model to reduce the cooling bill. The effect was groundbreaking. Applying the model reduced the power consumption by 40%.
Financial services have a great, in fact, a century-lasting tradition of keeping, managing, and processing the data. Traditionally it was done by smart people with great knowledge and unique skills.
Since the AI revolution many dull and repetitive tasks can be automated, these brilliant people can focus on delivering clients an added value and let the machines do the boring part.
Patrycja is a B2B Marketing Expert with experience in growing IT businesses since 2014. She creates and implements 360-degree marketing strategy. In her career, she focuses on lead generation based on marketing automation, collecting data, AB experiments and complex customer journeys. Business and self-development and psychology enthusiast.