Machine Learning is a term heard around the world these days. Industries like Retail, Healthcare, and Manufacturing are taking the best out of it. So does the leaders in Banking and Finance. There is enough time and room before the technology will truly explode. However, we can still talk about some real-world use cases and ways your business can benefit.
Machine learning in finance is rapidly developing – there are already dozens of options for its use in the financial sector. So why does the industry use AI for finance?
Artificial Intelligence helps banks more confidently issue credit to those who pass system checks. For this, programs and algorithms analyze all available information about a potential borrower, study their credit history, changes in their level of wages, and on this basis determines the reliability of the client and the security of the loan. Moreover, Chinese banks have already gone further and decided not to limit themselves to analyzing the data exclusively.
They began to introduce facial micro-expressions recognition technology. This allows them to find out if customers are lying about their financial situation when they come to take out loans. To do this, they developed AI systems that, with the help of smartphone cameras, detect minimal changes in facial expressions that are invisible to the naked eye. Thus, banks identify potential fraudsters, and they have already reduced their losses from unpaid loans by 60%.
This is a global task that is successfully solved through the introduction of AI and ML in Financial Services. When an algorithm can analyze all of the available structured and unstructured data (both internal from the company’s business processes and external such as customer requests and their actions on social media), a financial institution can discover both useful and potentially dangerous trends. It helps assess risk levels and allow people to make the most informed decisions.
Banks and payment systems have already been developing models to identify and block most fraudulent transactions. These models are built on the client’s transaction history as well as the client’s behavior on the Internet. Systems based on Artificial Intelligence that detect online frauds have been developed from Big Data technologies.
Fraudulent social engineering will also be reduced by Artificial Intelligence. For example, when an impostor pretending to be a bank employee fakes data, his activity will be neutralized. Such systems will make financial deception unprofitable for criminals, and most felonious schemes will “die.”
Many banks have implemented AI-based applications that allow customers to get answers to current questions. For example, a client can find out his expenses this month, the amount spent on food, credit card debt, the most affordable insurance, etc.
There are applications that, when connected to a payment system, analyze accounts. For example, for mobile communication or the Internet. These offer the owner more potential to save and make money. Sophisticated algorithms analyze user behavior online and allow financial institutions to develop more personalized and mutually beneficial offers. For example, if a customer is looking for opportunities to buy a car, the bank can develop a suitable loan offer after analyzing the customer’s financial situation.
Based on the analysis of the individual financial behavior of a client, banks are developing appropriate advertising or proposals. In this way, banks also receive information about the intentions of a customer or a potential customer. They get the opportunity to attract a new client who currently needs a personalized offer and can also take measures to withhold services if the client plans to refuse to work with this bank.
Systems with AI help automate and optimize the processes that occur in bank branches. In the future, the use of paper media will be completely abandoned. All information will exist in electronic form. Thus, AI can facilitate the work with internal operations, taking on routine operations and handling them much faster (without sacrificing accuracy) than any employee.
In addition, the ability of AI to collect and structure constantly changing information can increase the efficiency of reporting. AI can also design internal documentation and even a list of frequently asked questions; all of these will be continuously updated as needed.
Each time it processes a new flow of information, Artificial Intelligence learns and systematizes its knowledge. This allows it to assess the situation on the market and form the most profitable investment transactions via Big Data analytics. Many hedge funds have already made algorithmic trading their trump card. Besides the fact that the program is able to study, analyze, and systematize volumes of data that are too large for a human brain, the main advantage of using AI in the development of investment strategies is the fact that it knows nothing about typical human feelings and emotions. Greed, fear, and excitement are alien to this technology. Therefore, all of its assumptions are absolutely rational.
All these tasks are solved successfully with the help of Big Data analytics, including personal data, and Artificial Intelligence has obviously succeeded in this. However, it will be reasonable to ask a question about the ethics of collecting such information about users. At the moment, only 58% of users are calm about other people or technologies collecting their personal information. But if there is trust between a user and the other side, customers feel secure about the safety of their personal data.
It’s safe to say that AI and Finance have already proven to be a beneficial combination. AI and ML technologies are completely changing the way we interact with banks and financial institutions. According to a recent study, 77% of customers choose to pay with a credit card and only 12% with cash. That’s no surprise, because using a credit card makes renting an apartment, shopping online, or booking a flight much easier.
Banks make decisions on approving credit cards and loans using a variety of factors, credit history being one of the most important. Here are some examples of companies that leverage Artificial Intelligence to make smarter credit decisions.
Los Angeles-based ZestFinance introduced the Zest Automated Machine Learning (ZAML) platform. It is an underwriting solution that helps with credit assessment. Artificial Intelligence here makes it possible to assess borrowers that lack a credit history.
Based on a variety of data points, this platform provides an impressive level of transparency. Lenders can now improve the assessment of people who were considered too risky to lend to in the past. ZAML is an end-to-end platform that was built to be implemented and scaled quickly. Auto lenders that implemented ZAML have already cut their annual losses by 25%, according to ZestFinance.
A project from New York City provides an AI-powered underwriting platform that offers credit institutions more transparency and cuts losses. Scienaptic Ether already has more than 100 million customers to connect with an astonishing amount of data. The platform makes smart decisions on transforming data and learns more with each interaction, offering contextual underwriting intelligence. In collaboration with one leading credit card company, Scienaptic claimed to have saved $151 million in less than a month.
The Finance industry is getting more and more into machine learning to obtain more accurate, nimble models. Institutions can already improve trend searching, risk management, and future planning. Here are some examples:
This company offers Machine Learning capabilities and Data Analytics to the leaders of their respective financial institutions: Bank of America, Morgan Stanley, S&P Global, and J.P. Morgan. Kensho’s analytical solutions are based on the best of Natural Language Processing (NLP) and Cloud Computing. Their systems can answer sophisticated financial questions in English. S&P Global acquired Kensho for $550 million in 2018.
Cloud-based and on-site machine intelligence solutions are offered by this company to help organizations deal with multi-level challenges. Ayasdi is used to recognize, evaluate, and manage risks; forecast customer demand; and even help reduce money laundering. By using Ayasdi’s anti-money laundering (AML) solutions, one leading bank saved 20% on investigation expenses.
Today’s consumers are demanding more from the banking experience. According to Accenture, 54% of 33,000 banking customers want real-time tools for budget monitoring and spending adjustments and 41% of them are “very willing” to rely on ML-generated banking suggestions. Solutions like Chatbots use AI to come up with personalized financial advice and leverage Natural Language Processing to improve customer service. Here are specifics about how people’s banking experiences are being improved:
The developers of KAI, an AI-powered conversational platform, are improving customer experience in the finance industry right now. KAI offers self-service solutions that ultimately reduce the need to talk to an employee, lowering call center volume. More than that, their chatbots provide data-based recommendations and advice on everyday financial decisions. TD Bank Group is already planning to add Kasisto’s solutions to their mobile application.
This virtual assistant integrates with Facebook, Google Home, Amazon Alexa, SMS, the web, and mobile phones to offer customers a better banking experience. It could be as simple as support requests but can also expand to conversational management and personal financial banking. The need for boosting cybersecurity today is a must for every bank or financial institution. Artificial Intelligence and Machine Learning could make a huge impact in this area. Here are some companies that provide AI-based cybersecurity to industry leaders.
Major banks in the United States of America are already using Shape Security to reduce credit application fraud, scraping, gift card cracking, and credential stuffing. Shape Security’s software can easily tell the difference between real humans and bots via Machine Learning models that have been trained on billions of requests. Their network Blackfish uses AI-bots to detect compromised login credentials, notifying clients and banks about fraud happening at an exact moment. Shape Security helped one big bank prevent the hijacking of one million accounts in its first week of use.
These are the developers of cybersecurity solutions for many industries, finance being one of them. Their Machine Learning platform analyzes network data and offers probability-based calculations, detecting fraudulent actions before it can damage biggest financial institutions on the planet.
This concludes our list of AI applications in Finance and AI in Fintech. Now, let’s look closely at how the banking industry uses Machine Learning technology.
The Banking industry can be changed forever due to the influence of AI and ML finance projects. The scope of that change is astonishing; however, most banking institutions are still only beginning to implement AI in their processes. According to the Narrative Science and National Business Institute, 32% of financial service executives are already leveraging the following AI technologies: predictive analytics, recommendation engines, and voice recognition.
The biggest obstacle getting in the way of using AI and ML in banking is legacy systems. As a traditional industry, banking business leaders have been hesitant to change the technology processes that seem to work perfectly fine for them; consequently, this has slowed down the integration of AI.
Clients have become the real driver of banking with Machine Learning and Artificial Intelligence as they demand more from banks. Business leaders understand the necessity to stay ahead of the competition and consequently have been forced to evolve.
Machine Learning, as part of AI, helps improve the customer experience and allows businesses to rely less on human employees. Here are top five areas in banking that ML can significantly improve.
Credit Card Fraud Detection and Prevention is a challenge for any financial institution and is vital to a bank’s reputation and profit margin. Luckily, fraud prevention is the perfect area for Machine Learning to help. A bank has an enormous amount of data that can be analyzed by ML algorithms: spending habits, location, and client behavior. Machine Learning can instantly make a conclusion if something looks suspicious and alert the cardholder. This level of precision is impossible for a human employee, because hundreds of transactions must be analyzed in real-time. Moreover, an algorithm is less likely to make an error. When and anomaly is detected, the system could instantly require more information from the client or block the transaction. Banks can catch fraud as it happens by implementing ML.
The automatization of credit risk testing ultimately reduces losses for banks. Artificial Intelligence can also make predictions on issues that might occur, considering the history of transactions. In minutes, algorithms can process a gigantic amount of data — far more than employees are able to. Big Data technology provides individual portfolio holders deeper insights for better decision-making.
While security is very important for clients and businesses, they also value unique customer experiences and a variety of banking options. ML algorithms could easily keep in touch with individual client data. Eno, the assistant from Capital One, sends notifications to clients in instances when a card was charged twice or their tip in a café was too much money. Sometimes, getting your money back is a long and frustrating process; this assistant could be very useful in determining valid transactions from your credit card.
ML models can suggest banking tools to clients that could improve their financial decision-making. Since every bank offers a large amount of different services and options, it can be confusing at times. AI can provide clients with the best banking options available, helping them feel more important and satisfied.
With Machine Learning, the process of scoring credit history and assessing a potential borrower could be much easier than the methods that are popular now. ML can objectively evaluate a borrower without bias or emotions. Banks could understand the risks more clearly, based on the enormous amount of historical data available for every client.
Robotic process automation (RPA) can take over routine tasks, providing more precise services to customers without human errors. At the same time, employees could focus on more sophisticated tasks. Contract Intelligence (COIN) by JPMorgan Chase & Co has automated the processing of legal documents as well as data extraction. Machine Learning and Image Recognition were used to determine patterns in legal papers, reducing 360,000 hours of human labor a year to just a few hours. The use of chatbots is already quite popular. It is another way to achieve automation, improve speed, and lower the need for human involvement in such processes.
Another interesting point is the interest and even the demand from clients for the adoption of Artificial Intelligence and Machine Learning. Banking institutions can remain as conservative as they want, but their clients are expecting AI solutions from the bank. Increased levels of security and personalization are becoming the new standard for banks, and they must adhere to it. There are many barriers to AI adoption such as skill gap, legacy systems, and high costs; however, clients are demanding change as a price for their loyalty — therefore, banks must comply now and in the future.
Experts in the field of AI development recognize that humanity will still live to see the emergence of real Artificial Intelligence that will be smarter than its creator. And most importantly, such a system created with Artificial Neural Networks will think and make decisions independently. The first and most obvious consequence of this innovation is that the financial industry (as well as other major sectors) will begin to gradually abandon the participation of people in business processes.
Societe Generale, one of the largest banks in France, has already announced a reduction of 15% of its branches and 900 workplaces by 2020 as a part of cost reduction measures due to the more active introduction of digital technologies. Thus, financial industry employees who perform routine work will soon be replaced by Artificial Intelligence.
Leading Japanese companies have also declared their readiness to automate more than 30,000 workplaces. Banking leaders concluded that this was a necessary measure, since the traditional methods of doing business did not help increase profits. Japanese bankers realized that decisions made using Artificial Intelligence will minimize the costs of human labor.
At the same time, the introduction of AI is not a remedy for all possible errors — although, this algorithm cannot make the same error twice.
However, despite all the dangers and drawbacks, the statistics prove that sophisticated algorithms cope well with their tasks and increase profits of those who already use it for business.
Artificial Intelligence is definitely a promising area for investment. Companies that work in finance will remain competitive in the coming decades if they pay attention to these innovations today. While introducing Machine Learning algorithms and Artificial Intelligence in the financial industry, leaders also need to consider a system of protection against cyber attacks and ways to give users confidence in the safety of their data.
Originally posted here