AI Transformation in Financial Services
In financial services the competition for customer share of wallet is intense with firms looking for every advantage in marketing while fighting fraud, money laundering and other issues. Companies that are making extensive use of AI are reaping the benefits of increased customer satisfaction and loyalty, decreased fraud, and reduced regulatory penalties which adds to their bottom line.
These companies are using AI for a number of scenarios including anti-money laundering, fraud detection, credit risk scoring, and churn prediction. WooHoo AI, the open source and automation leader in AI, is empowering leading financial services companies to deliver AI solutions that are changing the industry.
Customer Churn Prediction AI Helps Retain Valuable Banking Customers
Some financial services customers become quite valuable as they generate fees on transactions and grow a portfolio of business over the years including banking fees, credit cards, home loans, personal loans and more.
Simple churn analysis uses rules based on known behaviors to identify potential churn risks. Rules-based systems, however, are inflexible and miss many customers who do churn and generate false positives that end up giving expensive incentives to customers who were not at risk to leave the bank.
AI is a great solution for customer churn prediction as the problem involves complex data over time and interactions between different customer behaviors that can be difficult for people to identify.
AI can look at a variety of data, including new data sources, and at relatively complex interactions between behaviors and compared to individual history to determine risk. AI can also be used to recommend the best offer that will most likely retain a valuable customer.
In addition, AI can identify the reasons why a customer is at risk and allow financial institution to act against those areas for the individual customer and more globally.
Credit Risk Scoring Personalizing Credit Decisions with AI
Banks and credit card companies use credit scores to evaluate potential risk when lending money or providing credit. Traditional credit scoring uses a scorecard method which weights various factors including payment history, debt burden, length of credit history, types of credit used, and recent credit inquiries.
This traditional method is based on broad segments and will deny credit to consumers without considering their current situation or other extenuating factors.
Traditional methods may also give credit to consumers, called churners, who are “gaming the system” and taking out a large number of reward credit cards but are not profitable for the issuers. For credit decisions there is also the additional regulatory burden that banks and credit card companies must explain to the consumer why they have been denied credit.
AI is a great solution for credit scoring using more data to provide an individualized credit score based on factors including current income, employment opportunity, recent credit history, and ability to earn in addition to older credit history.
This more granular and individualized approach allows banks and credit card companies the ability to more accurately assess each borrower and allows them to provide credit to people who would have been denied under the scorecard system including people with income potential such as new college graduates or temporary foreign nationals.
AI can also adapt to new problems, like credit card churners, who might have a high credit score, but are not likely to be profitable for the card issuer. AI can also satisfy regulatory requirements to provide reason codes for credit decisions that explain the key factors in credit decisions.
Anti-Money Laundering Stopping Crime with AI.
Money laundering is a huge problem for the financial services sector. According to the United Nations Office on Drugs and Crime, the estimated $2 trillion is “cleaned” through the banking system each year. Fines for banks who fail to stop money laundering have increased by 500X in the last decade to more than $10 Billion per year.
As a result, banks have built large teams of people and given them the time-consuming task of finding and investigating suspicious transactions which often take the form of numerous small transfers within a complex network of players. Investigation teams have used rules-based systems to find suspicious transactions, but the rules quickly become outdated and produce large numbers of false positives that still need to be reviewed.
AI, especially time series modeling, is particularly good at looking at series of complex transactions and finding anomalies. Anti-money laundering using machine learning techniques can find suspicious transactions and networks of transactions. These transactions are flagged for investigation and can be scored as high, medium or low priority so that the investigator can prioritize their efforts.
The AI can also provide reason codes for the decision to flag the transaction. These reason code tell the investigator where they might look to uncover the issues and help to streamline the investigative process. AI can also learn from the investigators as they review and clear suspicious transactions and automatically reinforce the AI model’s understanding to avoid patterns that don’t lead to laundered money.
Know Your Customer/Client (KYC)
Understanding the dynamics of your customer interaction with AI
Know Your Customer (KYC) is a key part of money laundering and anti-terrorism legislation. The Customer Due Diligence (CDD) process requires banks to file reports of suspicious activity. Almost two million such reports were filed in the United States alone in 2017 according to a study by the Royal United Services Institute for Defence and Security Studies — a U.K. think tank.
Failure to identify and file reports on suspicious transactions results in billions of dollars in fines for banks. Investigators looking into suspicious activity use a variety of tools including rules that flag frequent or international transactions or interactions with offshore financial centers. Unfortunately, with the volume and variety of transactions, rules-based approaches are not flexible enough to capture new patterns and produce large number of false positives that need to be reviewed.
AI is an ideal technology for finding anomalous patterns and identifying areas of risk especially where there are a large number of items of different types that need to be reviewed and potentially correlated. Machine learning can be used to perform analysis of transactions and can look for indicators of suspicious behavior including transactions with dubious jurisdictions, suspicious companies or known parties.
AI can also offer better insights into transactions through analysis of both structured and unstructured data. Natural Language Processing (NLP) techniques allow AI systems to search through communications to find additional signal including extracting metadata, identifying people or companies referenced, and categorizing the intent or purpose of the communication. All of these can help pinpoint suspicious transactions and help investigators as they investigate transactions.
Stopping Fraud in its Tracks with AI
Fraud is a huge problem in the banking industry. In 2016, the top 10 fraud types including wire fraud, card fraud, and loan fraud accounted for $181 Billion in annual losses, and the numbers are only increasing, according to fraud expert Frank McKenna.
Detecting and preventing fraud is a huge challenge for banks given the large variety of fraud types and the volume of transactions that need to be reviewed and manual or rules-based systems can’t keep up.
AI can be used to analyze large volumes of transactions to find fraud patterns and then use those patterns to identify fraud as it happens in real-time. When fraud is suspected, AI models can be used to reject transactions outright or flag transactions for investigation and can even score the likelihood of fraud, so investigators can prioritize their work on the most promising cases.
The AI model can also provide reason codes for the decision to flag the transaction. These reason codes tell the investigator where they might look to uncover the issues and help to streamline the investigative process. AI can also learn from the investigators as they review and clear suspicious transactions and automatically reinforce the AI model’s understanding to avoid patterns that don’t lead to fraudulent activities.