Artificial intelligence (AI) comes with numerous promises in several sectors, and the financial industry is no exception. Whether it is in improving customer experience or in automation, AI has significantly disrupted the financial industry and is now shaping its future.
Banking, for instance, is one sector slated to benefit significantly from incorporating AI systems. According to analysts, AI will result in more than $1 trillion in savings by 2030. Moreover, financial services players who will incorporate AI prudently will experience a 14% net gain in jobs and a 34% increase in revenues by 2022.
So, AI will continue to transform the future of several industries in different ways. Let’s look at 5 ways in which AI will shape the future of the financial services industry.
1. Customer Service
Financial institutions work within the service industry, and they all aspire to know their customers well. Fortunately, most of them have volumes of data that they only need to analyze for meaningful use. That is where AI comes in.
Customer service is and will always be an integral aspect of financial services. As banks automate, there is a growing fear that the introduction of AI-enabled bankers, such as bots, will reduce customer loyalty due to less personalization. However, that is not the case. Banks are increasingly using AI to increase customer satisfaction.
From chatbots to humanoids, banks are using AI to solve customer queries. Take the case of Bank of America’s chatbot, Erica. The AI-enabled chatbot provides the bank’s customers with 24/7 guidance through both text and voice. Moreover, the tool can perform routine transactions that help clients to experience hassle-free banking.
The advantage of chatbots over the conventional customer care staff is that they solve customer queries for the less-typical inquiries which don’t require consultations. Therefore, they provide instant answers to customers, saving on their time, thereby making them satisfied.
Wells Fargo is another bank that has joined the AI bandwagon by launching an app called Greenhouse. The bank aims to use the application to attract new clients and foster customer interactions. Specifically, the app targets millennials who hold the future of the bank because they are the majority.
With AI disrupting the financial industry, leading financial institutions have understood its importance and intend to harness it to gain a competitive advantage. They are now tracking the transactional data of their clients to understand their preferences to improve their banking experience.
2. Compliance and Fraud Detection
According to McAfee, a leading cybersecurity firm, cyber crime siphoned around $600 billion from the economy in 2017 alone. For financial institutions, avoiding online fraud is a big challenge. However, artificial intelligence can significantly help banks to be more efficient in detecting fraud.
For financial regulations that are machine-readable, AI, through predictive learning, can significantly help in compliance by identifying issues that require in-depth analysis.
Financial behemoths can use their enormous resources to partner with fintech companies, which will help them to update their legacy infrastructure. Notably, Citi Ventures has deployed big data and machine learning to monitor and thwart any criminal activities.
Money laundering will always be a priority concern in the banking sector, for obvious reasons. Algorithms can smoke out suspicious transactions and flag them accordingly using machine learning. Some banks have made significant steps in the realm of AI and money laundering by experimenting with digital identification innovations applicable to their AML (Anti-money-laundering) initiatives. These technologies can greatly bolster their AML compliance endeavors and enhance their transaction monitoring systems.
3. Process Automation
While process automation has become a necessity for banks, especially with RPA being the key driver, there is now a shift to the cognitive aspect of it. Using AI technologies, banks have increasingly incorporated more complex automation projects.
In 2017, JP Morgan Chase invested in a technology project called COIN (Contract Intelligence), which was to analyze legal documents to extract relevant clauses and data points. Results from the preliminary use of the technology showed that several agreements could be reviewed in seconds. In 2019, the bank’s technology budget hit $11.4 billion.
4. Access the Unbanked Population
For many years, banks have used credit scores to get the risk profile of borrowers. Additionally, they have also used systems such as FICO (Financial accounting and controlling) to obtain data on the earning potential of clients. However, for the unbanked population, the two approaches are inapplicable because these people lack a credit history.
However, AI can offer solutions. In emerging markets, Fintech startups such a Tala provide microloans to borrowers using a smartphone app. The company uses AI to assess a person’s support system and network as a guide for judging risk. Moreover, the application uses AI to obtain and analyze the repayment periods of users, enabling the company to create a clear risk profile of a borrower.
Equifax, a consumer credit reporting agency, launched a machine learning credit scoring system called NeuroDecision. The system paves the way for neural network modeling in credit scoring and can make predictions on the creditworthiness of clients with insufficient credit history.
Having a bank account is everyone’s aspiration. It provides a platform for active participation in economic matters. With the integration of AI technology in credit monitoring, the unbanked population can now access banking services that they were initially unable to get from conventional banks.
5. Smart Investment Banking and Trading
Artificial intelligence is an indispensable technology, especially in a competitive market like investment banking, where traders have to grab every advantage in the market. While investment bankers can create macros in Excel to assist them in financial modeling, the process is intensive and time-consuming. And as trade goes digital, investment banks have no option but to adopt machine learning.
BNY Mellon revealed that it was using 250 RPAs across sits portfolio to reduce the time and manual effort put by its employees. Through the initiative, the bank reported that it was able to achieve improvements in processing times and trade entry times. Therefore AI has significantly freed up the employees’ time at both the junior and senior levels.
Nowadays, investment banks are using automated trading systems with preset conditions to buy and sell orders. Since humans are prone to error, the algorithm-driven trading systems have proved to be efficient, especially in volatile markets.
In 2007, ING launched Katana, a tool intended to assist bond traders in their pricing decisions. According to the bank, the system can create statistical predictions to be used by traders in their daily engagements.
These are only a few, but very important, ways in which artificial intelligence is shaping the future of financial services. There are many other ways in which AI has disrupted the industry.