Artificial Intelligence is changing the way business leaders are operating their companies, disrupting entire industries. From Automotive and Manufacturing to Healthcare and Hospitality – the industries will never be the same as the new technology arrives.
When we are talking about AI/ML in Banking, we must mention that nearly 90% of the business leaders already believe that technology will offer a significant advantage for their organizations. Let’s take a look at the top AI product offerings in banking by vendors:
|Top AI Product Offerings in Banking|
|Fraud & Cybersecurity||20.2%|
|Financing & Loans||9.6%|
|Business Process Management||9.6%|
Risk Management and Compliance are usually an area in which bankers invest more than any other, so there is no wonder that they are near the top. But as you see Fraud Detection is clearly an important thing to focus on, let’s take a look at the most prominent use cases, to see if that number actually match the real situation.
Machine Learning in Banking: The Most Interesting Use Cases
These are the biggest and the most influential banks in the United States of America and their implementation of Artificial Intelligence and Machine Learning.
Probably the biggest supporter of Artificial Intelligence in Banking in the United States. They have at least seven AI-related press releases on different projects, most noticeably related to Cybersecurity and E-Commerce. Four years ago, in 2016, they partnered with Feedzai – fraud, and anti-money laundering company to add their software to the CitiBank operations.
The AI here analyses customer payment behavior and compare it to the usual payment activity, deciding whether something suspicious is happening. Feedzai will immediately alert the fraud analysis department on suspicious transactions before the transaction will be cleared.
JP Morgan Chase
The primary AI solution in this bank is a Contract Intelligence, more popularly known as COiN. This is a proprietary Machine Learning algorithm that automatically analyses documentation and extracts important information. This system exceeded the most daring expectations processing over 10 000 credit agreements in seconds, without any additional human input needed.
Five years ago, in 2015, they also introduced the Emerging Opportunities Engine. This technology is aimed to detect the customers that will engage in follow-up trading. Of course, they also have a 24/7 virtual chat assistant to improve their customer experience. JP Morgan Chase invested almost $10 billion in 2016, developing innovative solutions like that. Their capitalization grew by around $140 billion as a result, which is saying a lot!
This bank relies on AI-powered chatbot for their Facebook Messenger, which was developed by their AI Enterprise Solutions team in just two months. Implementing a virtual assistant to improve their customer experience resulted in easy operations for their client, without filling forms or time-consuming paperwork.
After successfully adding to their processes this chatbot in 2009, they launched their Startup Accelerator in 2014 to fund fintech startups. By doing this, Wells Fargo stays at the forefront of innovation having access to the latest AI/ML products. For example, their Predictive Banking mobile application alerting users of almost 50 different scenarios.
Bank of America
The pioneer of mobile banking in America introduced their virtual assistant Erica at the end of 2017. It was marketed as the “world’s most prominent payment and financial service innovation”, and no wonder it became leading chatbot application in 2019, having over 6 million users.
It is basically a mobile banking advisor, which could be downloaded by every client of the bank. The customer experience in Bank of America was improved dramatically, no more routine transactions and customer support overload!
This bank is focused on using the AI/ML to improve their processes. They partnered with the AI Innovation Group to introduce products for customer experience improvement, such as chatbots. Machine Learning algorithm they use, not only eliminates routine operations but even helps the bank’s employees answer and take action on rarely asked questions.
AI Fraud Detection and Prevention in Banking
At the initial stages of AI adoption, it was important for banks to improve customer service with solutions like virtual assistants and chatbots. However, there is an area that deserves to be addressed – Fraud Detection and Prevention, as the industry is very vulnerable to different types of hacks, scams, and frauds.
The number of cybercrime complaints is growing exponentially, as you can see here:
Business leaders should protect banking transactions and prevent any type of fraud by using all available technology and tools. 63% of financial institutions are certain that Artificial Intelligence has the ability to provide solutions that can indeed prevent fraud. Using supervised and unsupervised Machine Learning it is possible to distinguish fraudulent transactions from the valid ones. Let’s look closely at how ML is working to protect you from fraud.
The Most Common Fraud Detection Techniques Powered by ML
ML for Credit Card Fraud Analytics, Detection, and Prevention
Credit Card fraud is probably the biggest problem for banks in 2020. As the number of non-cash transactions is dynamically growing, business leaders should pay close attention to Credit Card Fraud Detection techniques.
Fraud Detection process powered by Machine Learning means investigating data by a Data Science team and developing a model that will reveal and prevent fraudulent activity. This model will decide on the legitimacy of the transaction. Using ML for credit card fraud analytics will provide such benefits as automatic detection, streaming in real-time, quicker verification methods and, additionally, identify hidden correlations.
Unsupervised Machine Learning methods that can be used for this are PCA, LOF, One-Class SVM and Isolation Forest, while supervised are Decision Trees, Random Forest, and KNN.
Identity Theft Detection
Identity theft had been a problem for banking for years, remaining at a high and stable level since 2015 and having a high percentage among general fraud complaints, as shown in this graphic:
To fight identity theft, patterns identification method is used. The actions of a particular client are being recorded in the database and being compared by algorithms to recent actions.
In the scenario when actions differ – fraud could be suspected, while the ML model becomes better with each transaction. This is basically is an anomaly detection case for ML experts. The following algorithms could be used to solve this problem: Isolation Forest, PCA, one-class SVM, and LOF.
Email Phishing Detection
Phishing emails are the spam letters sent with fraudulent intentions. In this case, criminals use websites and information that try to copy the look of the original.
This is a major threat to the Banking industry because it could influence the brand image and the client’s trust, that the organization had built for years. ML methods used to deal with it: Logistic Regression, SVM, Naive Bayes, and Extreme Learning Machine.
Top 5 AI Applications in Finance
Summing up, I would like to mention some of the most important use cases of AI in Finance in general, to see the whole picture of the industry the Banking is a part of.
AI offers a faster and way more precise assessment of a borrower, helping to make the right decision for financial institutions. In fact, AI is helping to cut costs on this process, as automobile lending companies in the USA save over 20% by using AI.
AI and risk management is a perfect match because the technology allows processing gigantic amounts of data in the shortest time. Cognitive computing is far better in managing information than humans. Crest Financial, a leasing company from the United States, used AI on the Amazon Web Services platform an experienced instant improvement in risk management.
The power of AI fraud detection
We mentioned Credit Card Fraud Detection, but there is the other major threat in the Finance industry – money laundering. Just like with credit cards, algorithms easily identify suspicious activities and make it much cheaper to investigate money laundering schemes. In one of the case studies, a 20% reduction in investigation spending was reported.
Investments driven by data are constantly rising in popularity. Alpaca Forecast AI Prediction Matrix by Bloomberg, for example, is a price forecasting tool. Combining real-world data from the market with a cutting edge advanced learning engine it makes accurate market predictions.
Robotic process automation is a good way to cut costs in any industry by taking over routine and time-consuming tasks, Finance is not an exception. AI software could process tons of documentation without human mistakes. Ernst & Young report up to a 70% cost reduction on RPA doing a routine activity.
As you can see, choosing the right development partner is a crucial element in implementing innovation in the industry, just as CitiBank did with Feedzai to succeed in their initiative. Luckily, there are some great companies in the market. SPD Group is an experienced software solutions developer that can handle your AI Fraud Detection project for the Banking industry.