Smartphones, smartwatches, smart houses – the list can go on and on. AI penetrates every aspect of our everyday life, making it more joyful and fun. We are no longer drivers but passengers in self-driving cars; we don’t have to clean the house as a robot vacuum does it for us; there is no need to do an extended search since smart algorithms offer us the relevant goods and services.
What was science fiction yesterday is commonplace today. It applies not only to domestic issues but also to other areas of activity. In this post, we will stop on the fintech branch and examine how AI brings it to the next smarter level.
Main Use Cases Of AI In Fintech
Below, I have collected the most interesting big data use cases in banking and fintech projects. This information might inspire you to breeze fresh air into your current business or pitch out a cool idea for launching something new.
Upcoming Payment Reminders
You may have noticed that a few days before you have to pay bills or repay the loan, you receive an essential reminder. Which way does the mobile app remember all the users and dates? Of course, with the help of artificial intelligence. A smart tool analyzes the data of the user account and notifies the client in advance.
Also, it prevents users from overdraft. Sometimes, it is difficult to refuse a loan that a bank “gifts.” It is even harder to resist spending money here and there. Irresponsible expenses could have caused problems if not for AI in banking. Now, smart technologies track the account and warn about low balance. Thus, users avoid payment delays, fines, and other troubles.
When you go into Internet banking from a new device, spend huge sums, or make too many payments in a short time, the bank will warn you of possible fraud. AI algorithms help financial institutions detect suspicious financial transactions and notify the user by phone, email, text, or push notifications. In some cases, users will even have to call the bank and personally confirm the transaction by answering additional security questions.
What’s more, the smart anti-fraud system detects weak points in the present setup and alarms the owner about a possible hacking. Primarily, AI works to eliminate holes in cybersecurity rather than fight cyber attacks. However, the latter is also on the duty list.
Below I have listed the essential to-dos of AI finance guardians:
- prevent network invasion
- ensure secure authorization
- examine firewalls and other security matters
- detect bots
- predict forced entry
Artificial intelligence in banking takes part in shaping the prices of certain goods. A smart algorithm studies regional features and estimates the income of the target audience. Therefore, prices for the same products may vary in different countries.
As an example, take Uber. Just recently, it has announced that it will use AI to bill clients. It means that the rate will be based not only on the apparent parameters, such as time, distance, demand but also on the passenger’s paying capacity. Also, the price will vary depending on the type of trip. Business trips are going to cost more.
Online services often analyze the user’s habits to offer suitable products and services. For example, if you spend a lot of time on real estate sites or car selling marketplaces, the bank may suggest a mortgage or car loan.
Not far to seek, look at Amazon or Netflix. Their algorithms impress with high accuracy. Not only do they offer the right products or content. They do this precisely at the moment when the customer is ready to make a purchase.
Virtual Financial Advisors
One good example of artificial intelligence is robotic advisors that help users manage their business plans, savings, and expenses intelligently. Here is what commercial bots can do:
- advise on the most suitable fintech services and products
- train financial literacy and cost-effective behavior
- search business-related data based on the user needs
- visualize user’s commercial performance (provide charts, diagrams, schemes)
Developers often rely on Natural Language Processing to create a chatbot. It helps correctly render voice requests and quickly find answers to questions of interest.
Credit Risk Assessment
It is hard work to analyze the borrower’s credit rating and possible risk. Human managers have to study many papers, contact third parties, and conduct in-depth research to draw up a client portrait.
So why not convey it to financial AI? Intelligent robots will do it faster and with better effect. They do
- credit scores,
- lending prognosis
- risk control
- estimation models
AI expertise adds value to commercial offers and speeds up the processing of requests.
Automated Refund Requests
When an accident occurs, people want to get a refund as soon as possible. However, calculating the amount to pay is not so simple. Take, for example, a road traffic accident. In this case, the insurance agent needs to consider many points:
- circumstances of the accident
- the year of manufacture of the car
- the technical condition of the vehicle before the accident
- the volume of premiums
- the period of insurance transfers
Manually doing all the stuff is too long and non-effective. Also, there is a high probability of error due to the human factor. For example, an expert assessment of the car may be entirely subjective and not reflect the real picture.
When robots get to work, things go faster and more clearly. They upload photos to the database, make smart visual analyses, calculate the range of refund pays. After that, the human employee only needs to review the data provided by robots and make a final decision.
Artificial intelligence and machine learning in banking digitize, interpret, and correct mistakes in printed documents. Today robots are good at reading text thanks to the technology of Optical Character Recognition. Business logic based on conditional formatting helps identify logical errors. It works on the principle of “if … then …”. For example, if you put a checkmark in the “Children” field, then the “Child Name” and “Child Age” fields should not be empty. Upon checking, robots revise the papers using the NLP model. In the end, they upload an improved contact version.
A growing field of contract analysis is smart blockchain-based contracts. Such contracts are concluded in the form of encoded mathematical algorithms. They are highly secure and fast to set up. However, the drawbacks are notable costs for implementation and the fact that the contract can’t be changed once it falls into the blockchain.
AI in finance helps make a list of clients who consider unsubscribing from a company’s services. To make such a list, the AI analyzes the client’s behavior, actions, and financial history. Among other things, the bot provides data about:
- refusal to receive informational messages of the company
- frequency and nature of complaints
- contacting support for various problems
Along with numeric data, the bot renders graphs and diagrams to show the periods of decline and increases in user activity. Also, it suggests possible factors that may have affected it.
With such a report, company managers take action to keep the client:
- work out the issues
- offer personalized services
- change the overall marketing strategy
Trading in the stock market requires in-depth knowledge, good intuition, and quick response. For good or ill, robots are superior to humans in all three respects. Based on data science in finance, they recognize patterns that are hard to catch by a human. Processing vast amounts of data, bots make sound decisions in a matter of seconds.
Brokers often use AI for short-run trades that take place in a fast price changing. When conducting such operations, you need to decide with lightning speed; otherwise, the transaction will not be profitable.
A good example is trading separate stocks before the price moves in the S&P 500 index. The mechanism takes the price move from the index and foretells how a particular stock price will change. Afterward, it gets sold or purchased instantly with the limit order set based on the forecast and hope that the price of a stock will change as predicted.
Advanced Research Tools
To create an effective economic strategy and build trust with customers, you need to spend a lot of time researching and collecting data. Machine learning tools allow accelerating not only this process but also collect data inaccessible to humans. For example, the recognition of images made by a satellite helps determine the points of sale at which the seller makes purchases most often or the company’s freight traffic along certain highways. The NLP tools analyze account statements and recognize the points that require immediate attention.
Examples Of AI In Banks And Fintech Startups
Now let’s review large banks and smaller fintech firms that utilize AI and ML in their business.
JP Morgan has switched from human to automotive loan agreement processing. For that, it has created a unique Contract Intelligence platform. AI now replaces a whole branch of employees that altogether spent 360 hours to complete the task.
Wells Fargo invests in creating the bank-based AI department. The AI teams’ main task is to implement smart technologies that will bring user experience to a higher level. In particular, they will work on fraud prevention and personalized services.
Bank of America introduced Erica, a robotic assistant that will consult users through mobile apps and some ATMs. Erica can manage transactions, assist in everyday banking, show, pay, cancel bills, answer financial-related questions, and do many other things.
CitiBank invests in fintech startups that specialize in AI and machine learning in finance. Recently, they have contacted Feedzai that takes care of cybersecurity and anti-fraud issues. They have also piled into Clarity Money that guides users through the fintech ecosystem, and helps choose the relevant products.
DreamQuark specializes in big data and AI strategies, mainly for banks and insurers. They help large-scale companies increase their revenue by using smart algorithms for managing financing activities.
DataVisor develops a robust cybersecurity system that adapts to specific business needs and prevents malicious attacks. Also, a smart algorithm reports on trouble spots that may be a potential target of the attack.
What Is The Future In Finance AI?
Artificial intelligence in financial markets strides ahead promptly. If a few years ago, it could perform only typical tasks, now it copes with complex assignments that require in-depth analysis and data comparison. Given this, we can safely say that irtificial intelligence will replace many human functions. in the future This statement applies not only to low-skilled labor but also to activities requiring vast knowledge and skills.