How AI Helping FI to Serve their Clients Better in this Current Climate
With the recent changes our economy and business are experiencing, companies are relying more than ever on AI to maintain sustainability in their organizations. Banks, insurers and lending platforms are seeing a rise in digital transactions, and in order to establish their online presence, AI solutions will be instrumental.
In this article, we look at current examples of how AI solutions are infiltrating the financial and banking industries and some of the up-and-coming trends to be on the lookout for in the near future.
Reporting and Analysis
AI helps with credit decisions by processing data received from customers and clients and building accurate and predictive models that enhance decision making. Accurate forecast predictions are also important for speed of the service and business protection. Machine learning predictions help financial companies utilize existing data to identify risks and ensure better information for future planning.
AI and ML are taking the place of manual analysis very quickly. Using machine learning, which learns over time can reduce the probability of error, while the quantity of data is increased ten-fold. Artificial Intelligence can provide a faster, more accurate assessment of a potential borrower, at less cost, and can take into account a wide variety of factors. Credit scoring provided by AI is based on more complex and sophisticated rules compared to those used in traditional credit scoring systems and can help lenders distinguish between high default risk applicants and those who are credit-worthy but lack an extensive credit history.
Digital banks and loan-issuing apps are already using machine learning algorithms to use alternative data (e.g., smartphone data) to evaluate loan eligibility and provide personalized options and automobile lending companies in the U.S. have reported success with AI for their needs as well. For example, this report shows that bringing AI on board cut losses by 23% annually.
Fraud Detection and Cybersecurity
Every business aims to reduce risk, including financial institutions. AI is on top when it comes to security and fraud identification. AI is especially effective at preventing credit card fraud, which has been growing exponentially in recent years due to the increase of e-commerce and online transactions. Fraud detection systems analyze clients’ behavior, location, and buying habits and trigger a security mechanism when something seems out of order and contradicts the established spending pattern.
It can use past spending behaviors to identify odd behaviors, such as using a card from another country just a few hours after it has been used elsewhere, or an attempt to withdraw a sum of money that is unusual for the account in question. Another excellent feature of fraud detection using AI is that the system is always learning and improving. If a red flag is raised for an approved transaction and a human being corrects that, the system can learn from the experience and make even more sophisticated decisions about what can be considered fraud and what cannot.
Banks also employ artificial intelligence to reveal and prevent another financial crime: money laundering. Machines recognize suspicious activity and help to cut the costs of investigating the alleged money-laundering scheme by up to 20% as one Case study suggests.
Aggregators like Plaid (which works with financial giants like CITI, Goldman Sachs and American Express) take pride in their fraud-detection capabilities. Complex algorithms can analyze interactions under different conditions and variables and build multiple unique patterns that are updated in real time.
Financial Advisory Services
According to the Pwc Report, we can look forward to more robot-advisors in the future of the financial industry. As the pressure increases on institutions to reduce commission rates on individual investments, machines may do what humans won’t: work for a single down-payment. Another emerging field is bionic advisory, which combines machine calculations and human insight to provide options that are much more efficient than what their individual components provide. An excellent balance between AI and human capabilities will make AI an element in decision-making that is as important as the human viewpoint is the future of financial decision-making.
Investment companies have been relying on computers and data science to determine future patterns in the market for years. Trading and investments depend on the ability to predict the future accurately. Machines are great at this because they can crunch a huge amount of data in a very short amount of time. Machines can also be taught to observe patterns in past data and predict how these patterns might occur in the future. A machine can be taught to study the anomalies in data sets, such as that of the 2008 financial crisis, to find triggers and indicators and incorporate them into future forecasting as well. What’s more, is that depending on individual risk appetites, AI can suggest portfolio solutions to meet each person’s demand. For example, a person with a high-risk appetite can consult with AI solutions to make decisions on when to buy, hold and sell stock. One with a lower risk appetite can receive alerts for when the market is expected to fall and can thus make a decision about whether to stay invested in the market or to move out.
These types of data-driven investments have been rising steadily over the last 5 years and closed in on a trillion dollars in 2018. It’s also called algorithmic, quantitative or high-frequency trading.
This kind of trading has been expanding rapidly across the world’s stock markets, and for good reason: artificial intelligence offers multiple significant benefits.
Intelligent Trading Systems monitor both structured and unstructured data in a fraction of the time it would take for humans to process it. And nowhere is the saying “time is money” truer than in trading where faster processing times mean faster decision-making which, in turn, means faster transactions.
The predictions for stock performance are more accurate because algorithms can test trading systems using past data, bringing the validation process to a whole new level before pushing it live.
AI puts together recommendations for the strongest portfolios depending on a specific investor’s short- and long-term goals; multiple financial institutions also trust AI to manage their entire portfolios.
AI and Personalized Banking
Artificial intelligence truly shines when it comes to exploring new ways to provide additional benefits and comfort to individual users.
In banking, AI powers the conversive chatbots that provide clients with comprehensive self-help solutions while reducing the workload on physical call-centers. Voice-controlled virtual assistants are also gaining traction fast, which is no surprise. Boasting a self-education feature, these smart systems are literally getting smarter every day, so you should expect tremendous innovations in this particular area. Both tools can check balances, schedule payments, look up account activity and more.
There are also numerous apps that offer personalized financial advice and can help individuals achieve their personal financial goals. These intelligent systems track income, essential recurring expenses, and spending habits and formulate with an optimized plan complete financial tips and recommendations. Larger banks have also launched similar mobile banking apps that offer features such as bill reminders and 24/7 support to its clients.
AI is establishing automation in areas that require intelligence, analytical and clear-thinking while maintaining high levels of customer service using these unmatched resources which can result in enterprise wide savings of time and money.
True digital transformation, however, requires more than simply applying the latest and greatest technology. It requires a customer-centric, outside-in perspective to enable the design of digital solutions that drive customer loyalty, engagement, consumption and satisfaction.