There is a potential source of confusion regarding what we refer to with the term Finance Domain. On the one hand, there is a function called finance that is common to all business enterprises, in every industry. On the other hand, there is a financial services industry. Then we have a term that refers to all financial institutions like Banking, Brokerage Houses, and Insurance etc. The knowledge of finance domain enables possible career paths within the financial services industry or with financial institutions.
The Finance Function:
The finance function encompasses a variety of functions, activities and processes. It consists of financing functions, budgetary functions, risk and return management, cash flow management, cash management, financial management, risk and governance and many more associated functions.
What is Fintech?
The financial industry follows technological advancement with keen interest. Big banks such as JP Morgan have been early adopters of disruptive technologies like Blockchain.
Artificial Intelligence (AI) is a paradigm-shifting technology that is seamlessly changing the way we live, move, interact with each other, shop. Finance is no exception, and the industry is just starting to peak at the tip of the iceberg.
Fin-tech is the name given to use-cases of cutting-edge technology to the financial industry.
Let’s look at the Use-Cases where AI can bring the difference.
Artificial Intelligence provides a faster, more accurate assessment of a potential borrower, at less cost, and accounts for a wider variety of factors, which leads to a better-informed, data-backed decision. Credit scoring provided by AI is based on more complex and sophisticated rules compared to those used in traditional credit scoring systems. It helps lenders distinguish between high default risk applicants and those who are credit-worthy but lack an extensive credit history.
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 (databases, spreadsheets, etc.) and unstructured (social media, news, etc.) data in a fraction of the time it would take for people to process it. And nowhere is the saying “time is money” truer than in trading: faster processing means faster decisions, which in turn mean faster transactions.
The predictions for stock performance are more accurate, due to the fact that algorithms can test trading systems based on past data and bring 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.
The business news outlet, Bloomberg, recently launched Alpaca Forecast AI Prediction Matrix, a price-forecasting application for investors powered by AI. It combines real-time market data provided by Bloomberg with an advanced learning engine to identify patterns in price movements for high-accuracy market predictions.
In the banking sector, AI powers the smart chatbots that provide clients with comprehensive self-help solutions while reducing the call-centres’ workload. Voice-controlled virtual assistants powered by smart tech like Amazon’s Alexa are also gaining traction fast, which is no surprise: boasting a self-education feature, they get smarter every day, so you should expect tremendous improvements here. Both tools can check balances, schedule payments, look up account activity and more.
A number of apps offer personalized financial advice and help individuals achieve their financial goals. These intelligent systems track income, essential recurring expenses, and spending habits and come up with an optimized plan and financial tips.
The biggest US banks, such as Wells Fargo, Bank of America and Chase, have launched mobile banking apps that provide clients with reminders to pay bills, plan their expenses and interact with their bank in an easier and more streamlined way, from getting information to completing transactions.
Forward-thinking industry leaders look to robotic process automation when they want to cut operational costs and boost productivity.
Intelligent character recognition makes it possible to automate a variety of mundane, time-consuming tasks that used to take thousands of work hours and inflate payrolls. Artificial intelligence-enabled software verifies data and generates reports according to the given parameters, reviews documents, and extracts information from forms (applications, agreements, etc.).
Employing robotic process automation for high-frequency repetitive tasks eliminates the room for human error and allows a financial institution to refocus workforce efforts on processes that require human involvement. Ernst & Young has reported a 50%-70% cost reduction for these kinds of tasks, and Forbes calls it a “Gateway Drug To Digital Transformation”.
A leading financial firm, JP Morgan Chase, has been successfully leveraging Robotic Process Automation (RPA) for a while now to perform tasks such as extracting data, comply with Know Your Customer regulations, and capture documents. RPA is one of ‘five emerging technologies‘ JP Morgan Chase uses to enhance the cash management process.
Digital Financial Coach/Advisor
Transactional bots are one of the most popular use cases in AI, probably because the range of applications is so broad — across all industries, at several levels.
In finance, transactional bots can be used to offer users finance coaching/advising services.
Think of them as digital assistants helping users navigate their finance plans, savings, and spending’s. Such service increases user engagement and improves the overall experience of the user with the financial product they are interacting with.
Digital assistants can be built using Natural Language Processing (NLP), a type of machine learning model that can process data in the format of human language. A layer of product recommendation model can be added, allowing the assistant to recommend products/services based on the transactions that occurred between the algorithm and the human user.
An example of this application has been deployed by Sun Life which created a virtual assistant, Ella, to help users for Benefits and Pension by allowing them to stay on top of their insurance plans. The assistant sends users reminders based on user data like “Wellness benefits about to expire” or “Your child will be off benefits soon.”
Digital assistants can also be used in other finance-related scenarios: dividend management, term life renewals, transaction limit approaching or cheque cashed notifications.
Automated Claims Processes
The insurance industry as we know it functions on a standard process: clients subscribe insurance, for which they pay. If the customer has a problem (sickness for health insurance, a car accident for automobile insurance, water damage for a housing insurance), she needs to activate her coverage by filing a claim. This process is often lengthy and complicated.
Transactional bots can transform the user experience into a more pleasant process.
Enhanced with image recognition, fraud detection, and payout prediction, the entire user journey is upgraded — less friction, fewer costs for the company, less operational tasks (calls, background checks) and fewer errors all in all. The entire process takes less time and becomes a seamless experience for both customers and the insurance company staff.
What the bot does is to take charge of the entire cycle: it walks the customer through the process, step by step, in a conversational format.
Swishbot, a transactional bot we built from scratch, can be used by an insurer for their customers
It asks for videos or photos of the damage and uploads them to the database. It takes in all the information required for the processing of the claim. The bot can then run the application through a fraud detection method, looking for anomalies and non-compliant data.
It then moves on to the adjustment model where it provides a range of values for payout. Once all data is set, human intervention can be included for auditing purposes. The bot can at this point calculate and propose payout amounts, based on a payout predictor model it has been trained on.
This application is a three in one machine learning solution that holds the potential to relieve a high pain point in the industry.
It is what Lemonade, a New York-based insurance startup, has set as a mission. On the homepage of their website, they ask users to “forget what you know about insurance” clearly announcing the disruption they are bringing to the industry through the use of AI. The company raised USD 180 million since its creation in 2015.
Challenges of using AI in this domain:
- Finding Useful Data:
One of the biggest problems is scarcity of data. Yes, you read that correctly. While companies have access to more data than they’ve ever had before, datasets that are useful for AI applications in the financial sector are few and far between. Those would be datasets where the data is labelled.
“Everyone knows the best AI we’ve had is around supervised learning, and that all requires labelled data, and there’s real lack of labelled data in financials,”
- Finding the Right Talent:
The second big problem is scarcity of talent for AI model development. It is challenging to find people who not only understand AI technology but also have domain expertise specific to Finance.
- Touch Compliance Standards:
Finally, whenever a financial services firm invests in a new technology, it has to answer some tough questions around compliance. It took a while before the cloud services market matured enough to meet the financial industry’s compliance requirements, and she expects to see the same dynamic with AI.
Financial compliance standards don’t make AI research impossible for companies like Morgan Stanley, but they do make innovation either slower or more expensive than in lighter-regulated industries.