Artificial intelligence is a field of computer science that focuses on making machines intelligent by developing computer programs that can replicate human intelligence to a certain degree and potentially beyond.
This much talked about technology also has many possible applications within the financial industry. The four most prolific financial use cases for AI are in customer service, financial advice, AML pattern recognition, and fraud detection.
Chatbots for customer service
According to the Accenture Banking Technology Vision 2017 report, “artificial intelligence is becoming a company’s spokesperson”. For financial services companies, that means utilizing AI-enabled chatbots that can handle the wave of customer service inquiries that businesses have to handle on a daily basis. The most simple forms of AI have already been in place for years, in the form of voice recognition on automated customer service hotlines but with the move from customers towards live chat, AI will play an increasing role in customer service through the use of chatbots.
Chatbots are AI-based automated live chat systems that can simulate human conversations without human intervention. Chatbots function by identifying the emotion and context of the text written by the customer and respond with the most appropriate reply. Chatbots also collect masses of data on user behavior and habits, which they learn to adapt to future customer inquiries, including being able to better gauge customers’ moods.
Chatbots enable financial institutions to reduce the costs of their customer service operations without reducing the amount or quality of customer service on offer. In an increasingly competitive banking landscape, where mobile banking startups are competing with established high street banks over deposits, high-quality customer service is more important than ever for customer acquisition and retention.
Globally, 79 percent of financial services professionals believe that artificial intelligence will change the way financial institutions will collect data and interact with their customers while 76 percent believe that within the next three years, financial institutions will use artificial intelligence as their main method for interacting with their customers, according to Accenture.
Customized financial advice
Artificial intelligence, today, can go beyond purely dealing with customer service inquiries. AI can also provide customized financial advice through the use of big data covering the financial institution’s product offering as well as past data about the customer.
For example, if a banking customer wants to apply for a personal loan, the AI can incorporate a wide range of customer data points such as credit score, social media feeds, spending patterns, etc to determine a suitable interest rate and the amount the customer can borrow.
Alternatively, investment management companies can leverage advanced natural language generation (NLG) to automatically write investment commentary that notifies their investors about the performance of their funds. Through applying this form of AI, investment managers can publish reports in real-time and share them with their distribution channels, thereby saving time and money analyzing data and producing these reports. Best of all, these reports can also be tailored to each individual investor.
Customized financial advice can also come in the form of AI-driven investment advice itself. Fintech startup Pefin, for example, uses feed-forward neural networks that analyze answers from consumer to provide customized investment advice to provide affordable personal finance advice to the “less affluent”.
Pefin founder and CEO Ramya Joseph told CNBC: “[Robo advisors] are trying to execute a transaction, while we are trying to manage your finances. Investing is optional with us, and we’ll help you if we think it’s the right move for you […]. The longer you stay in the system, the more we learn and the better we get at predicting and advising you because we can tie it back to your habits and what works for you.”
AML pattern detection
While the customer-facing side of business is probably the most talked about application for AI, the technology can also provide a useful service in anti-money laundering (AML) pattern detection. Money laundering has always been high on the agenda of financial regulators and law enforcement agencies, which is why banks have always tried to identify potential money laundering activities as soon as they occur. With the help of artificial intelligence, this will be made even easier.
In the majority of money laundering cases, criminals hide their actions through a series of steps that will make it look like the funds that stem from illegal sources have been earned legitimately. That one of the primary reasons is why banking customers have to go through lengthy onboarding and KYC processes. However, criminals set up legitimate businesses that can pass these processes to then proceed to launder their funds through the financial system.
Until recently, financial institutions have been using rule-based software programs to identify potential money laundering activities. Now, they are switching to AI-based programs that deploy a much more intelligent approach to discovering anti-money laundering patterns.
According to Jose Tabuena at Compliance Weekly, “advances in machine learning and affordability of large-scale computing resources enable more sophisticated anomaly detection.” This is what new AI systems can provide. However, Tabuena, also argues that “in order to accomplish this new approach to threat detection, compliance professionals will need greater knowledge of core operational processes to understand a potential compliance incident’s business context. In short, the human element remains critical.”
Fraud detection is another area where artificial intelligence will be able to play a key role in the financial services industry. Global credit card fraud alone will cost the financial industry over $35 billion by 2020 according to The Nilson Report. This is reason enough for financial institutions to invest in new technologies to minimize these losses.
An example of an AI-based fraud detection tool is the FICO Falcon fraud assessment system. It is based on a neural network and deploys deep learning methods to “prevent, detect, and manage fraud across the enterprise” that involves credit card, debit card, mobile and e-payments. The FICO Falcon Fraud Manager can be used to detect fraud, develop strategies to prevent fraud and to execute fraud-related decision across an institution’s product suite.
FICO’s system is one of the early programs incorporating AI technology in fraud detection but given the negative impact fraud has on the financial industry, many similar AI-based systems are likely to follow.
Artificial intelligence is poised to become one of the most impactful technologies to disrupt the financial service industry in the coming five to ten years. AI will help financial institutions to communicate with the customers, build better customer relationship and reduces the cases of money laundering and fraud.