AI In Fintech: Use Cases, Applications And Future Of AI In Financial Services

AI in fintech

In the last few years, AI in Fintech has seen some significant advancements. As a result, AI is rapidly altering the way business is conducted. Over the years, finTech agents and some traditional financial industry businesses have grown stronger. Many financial institutions have been impacted, but many more are quickly adapting to provide financial services that are tailored to the new reality of the world.

 Certain finance companies were bolstering their business models with cutting-edge and inventive HiTech solutions. This procedure has now been speeded up. Artificial Intelligence (AI) and Machine Learning (ML), in particular, are altering how many things are done in the finance industry. 

As more financial transactions are conducted through apps, businesses can gain valuable insights from new data points. As a result, new disruptive technologies open up a plethora of opportunities for both individuals and businesses.

Almost everyone, however, believes that AI and machine learning are only for large corporations with a large pool of capital and a large number of tech experts. There isn’t a single thing that could be further from the truth. FinTech firms of all sizes are putting these technologies to work, paired with powerful apps, for a variety of purposes.

In this article, we discuss some of the most important ways in which FinTech companies are using AI and ML. Let dive in…

AI in Fintech: Use Cases of Artificial Intelligence and Machine Learning

Financial businesses can harness the power of technologies like an Artificial Neural Network or other disruptive tools to construct strong products and decision-making solutions to innovate in the financial services industry by leveraging computer-based tools that rely on Big Data analytics. This is causing significant changes at both the organizational and personal levels.

In FinTech, AI has the ability to assist businesses in achieving their growth goals, gaining a competitive advantage, and becoming more relevant to their customers. It can also assist them in lowering operational costs and streamlining internal processes. Users can benefit from this by better managing their personal finances.

These are just a few examples of the most important uses of AI and ML algorithms in finance.

  • Improved financial decision making
  • Security & fraud detection
  • Asset management
  • Customer support
  • Insurance
  • Loans
  • Personalization

Improved Financial Decision Making

FinTech apps are introducing new and exciting ways for consumers to handle data. Analyzing data through apps becomes simple thanks to the power of data science and visualization tools, transforming it into digestible insights. As a result, users can better utilize complex data to make better financial decisions. 

Security & Fraud Detection

Financial cybercrime will increase as digital transformation processes take over the planet. The bright lining is that owing to AI and machine learning, businesses and individuals can now protect themselves and their accounts.

The terms “cryptocurrency” and “blockchain” are frequently used to refer to financial security. However, in the not-too-distant future, AI and machine learning will be associated with digital security and anti-money laundering technologies. Algorithms can detect questionable conduct and, even better, alert consumers. Because these technologies can continuously monitor anomalous patterns, there is no need to stay watchful 24 hours a day, seven days a week. Users can keep tabs on what’s going on behind their backs while remaining confident that their assets are secure. 

These technologies have also had a significant impact on the detection of other illegal activities such as money laundering. Governments and other institutions now have the ability to track down corruption networks using an army of bits and bytes thanks to AI and machine learning. 

Asset Management

For a long time, investment funds have relied on complex algorithms to create reliable forecasts and simulations. As a result, the asset and wealth management industry has been able to restructure many processes and provide new services such as wealth management tools. FinTech companies have noticed this and are incorporating these solutions into their apps so that users can benefit from them.

Users of the app may now access their bank statements and make crucial transactions from any of their devices. Most significantly, AI and machine learning technologies give users the option of doing so, decreasing the number of intermediaries. As a result, wealth management has been able to eliminate redundant procedures, lowering operating costs.

AI in fintech

Customer Support

One of the most well-known AI applications is bots. ML algorithms have only recently begun to gain traction, despite the fact that they have been around for some time. We are now seeing the rise of powerful chatbots that can interact with customers and respond to a variety of customer requests in real-time.

Bots are being used by FinTech organizations as a primary avenue for resolving consumer complaints. Some of the most common ML solutions include Robo advisers and automated customer service. Chatbots have proven to be effective in lowering expenses and increasing customer satisfaction. 

Insurance

The use of AI and machine learning to transform the way insurance policies are evaluated is one of the most inventive applications of AI and machine learning. FinTech apps are being used to measure risk levels because this industry is significantly influenced by financial instruments. Companies can determine a person’s risk level based on their activities.

The auto industry has successfully implemented this strategy. This industry now has the ability to measure a person’s risk level by measuring their driving skills using a mobile app thanks to a mix of IoT technology and FinTech app development.

Smart contracts, which make use of blockchain and artificial intelligence, are also being utilized to innovate in the insurance business.

Loans

This is most likely the most common way in which FinTech firms gain from HiTech. The ability to use someone’s financial habits and credit exposure to compute credit scoring, making the underwriting process more efficient without the need for human participation, has sparked a wave of money lending apps around the world.

Loans can be processed faster and more efficiently using AI and machine learning. Additionally, because of a better customer risk profile approach, they are more accurate than traditional underwriting. Some experts even suggest that this could benefit customers by removing the biases that often arise when humans make decisions. 

Although the latter is correct, negative biases can also exist. Agents who use these mechanisms must ensure that they have everything worked out in terms of credit scoring; otherwise, they risk alienating a large segment of their clientele.

Application of AI In Fintech

1. Algorithmic Trading — the most advanced ML you will never see.

The majority of algorithmic trading applications take place behind the closed doors of investment banks and hedge funds.

Trading frequently entails assessing data and making quick decisions. Machine learning algorithms are excellent at evaluating data of any amount or density.

The only requirement is that there be enough data to train the model, which trading provides in spades (market data, current and historical).

The program recognizes patterns that are difficult for humans to notice, reacts faster than human traders, and can execute trades automatically based on data insight.

A market maker searching for short-term trades based on rapid price fluctuation could employ such a model. These are time-sensitive processes, and the model delivers the necessary speed.

An example of this is trading individual stocks against the S&P 500 index, which is a well-known leading indicator (i.e. stocks follow the index). The algorithm uses the index’s price movement to forecast individual stock movement (ex: Apple). The stock is then immediately bought (or sold) with a limit order at the prediction level in the hopes that the stock will reach that price.

2. Automated Claims Processes

The insurance sector, as we know it, follows a regular procedure: customers purchase insurance and pay for it. If the customer has a problem (such as a disease for health insurance, a car accident for auto insurance, or water damage for home insurance), she must file a claim to activate her coverage. This is a time-consuming and difficult task.

Transactional bots can transform the user experience into a more pleasant process.

The entire user journey is improved with picture recognition, fraud detection, and payout prediction, resulting in less friction, lower company costs, fewer operational chores (calls, background checks), and fewer errors overall. For both customers and insurance company employees, the entire process takes less time and becomes a more seamless experience.

The bot takes responsibility for the entire process, guiding the customer through each step in a conversational fashion.

This application is a three-in-one machine learning solution with the potential to alleviate a major industrial pain point.

Lemonade, a New York-based insurance startup, has made it one of its goals. They advise visitors to “forget what you know about insurance” on their website’s homepage, plainly proclaiming the disruption they are bringing to the market through the application of AI. Since its inception in 2015, the company has raised USD 180 million.

3. Digital Financial Coach/Advisor

Transactional bots are one of the most popular AI use cases, owing to their wide range of applications – across all industries and at all levels.

Transactional bots can be used in finance to provide users with financial counseling and advice.

Consider them digital assistants that assist users with their financial goals, savings, and expenditures. This type of service boosts user engagement and enhances the user’s entire experience with the financial product they’re dealing with.

Natural Language Processing (NLP), a type of machine learning model that can process data in the format of human language, can be used to create digital assistants. 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.

A layer of product recommendation model can be added, allowing the assistant to recommend products/services based on the algorithm’s and human user’s transactions.

Sun Life, for example, has implemented a virtual assistant named Ella to assist users with Benefits and Pensions by allowing them to stay on top of their insurance plans. The assistant sends users reminders based on their data, such as “Your child’s benefits are about to expire” or “Wellness benefits are about to expire.”

Other financial scenarios where digital assistants can be useful include: Dividend management, term life renewals, transaction limit nearing, or check cashed notifications are all things that you should be aware of.

4. Underwriting, Pricing & Credit Risk Assessment

Underwriting services are provided by insurance companies, primarily for loans and investments.

An AI-powered model can provide a real-time evaluation of a client’s credit risk, allowing advisors to construct the best possible deal.

The use of artificial intelligence (AI) for underwriting services improves the efficiency of proposals and the client experience by speeding up the process and turnaround time of such operations.

Manulife, a Canadian financial services company, is the country’s first to use AI for underwriting, making it “faster for many Canadians to buy basic life insurance, a key to addressing Canada’s “protection gap.”

The insurance firm employs a specialized AI, known as the Artificial Intelligence Decision Algorithm (AIDA), that is trained on prior underwriting procedures and payments and can be classified in a variety of ways, including high loss payout or price.

This method is not limited to insurance; it may also be applied to credit scoring for loans.

5. Transaction search & visualization

In banking, chatbots can be used to focus on search tasks.

Managers grant the bot access to the users’ transactional data (banking transactions), and the bot uses natural language processing to decipher the meaning of the user’s request (a search query). Balance inquiries, spending habits, basic account information, and other requests may be made. Following that, the bot will process the requests and display the results.

For their client base, Bank of America uses a bot called Erica as a digital financial assistant. The AI-powered bot gained a lot of traction rapidly, with one million users in just three months.

The bot provides a user-friendly transaction search feature that allows customers to search their historical data for a given transaction with a specific merchant, saving them the time and effort of searching through their bank accounts. The bot also calculates overall credit and debt balances, which customers previously had to accomplish on their own with a calculator.

6. Client Risk Profile

Client profiling based on risk score is an important component of the job of banks and insurance companies.

AI is a great tool for this since it can automatically categorize clients based on their risk profile, which ranges from low to high.

Advisors can select to associate financial products for each risk profile and offer them to clients in an automatic manner based on the classification process (product recommendations).

Classification models such as XGBoost or Artificial Neural Network (ANN) are trained on historical client data and pre-labeling data provided by advisors for this use case, eliminating data-induced bias.

7. Contract Analyzer

In the finance industry, contract analysis is a common internal duty. This routine task can be delegated to a machine learning model by managers and consultants.

Hard copy documents can be digitized using optical character recognition (OCR). The contracts can then be quickly interpreted, recorded, and corrected using an NLP model with layered business logic.

Business logic is a conditional formatting feature similar to that found in Microsoft Excel. Formulas, such as “if this box is checked, then this one should be blank,” can be added to the model. Existing contracts can be used to train the model and teach it how to behave with such content.

Because of the recurring structure of contracts, the model’s outcome is extremely accurate in this example.

JP Morgan has leveraged the power of AI in this application, releasing 360,000 hours (yearly) from the workload of its staff in only a few seconds.

These tools aid contract analysis, while blockchain-based smart contracts, a paradigm-shifting update in contract management, are gaining traction.

8. Churn Prediction

Churn (or attrition) rate is a key KPI across all industries and businesses. Companies need to retain clients, and to do so, predicting coming churn can be extremely helpful to take preventive actions.

Managers can help with this mission by giving a prioritized list of clients who are considering canceling their policies. The manager can then address the following items on the list: provide a higher level of service or a better product.

In this scenario, the model is based on customer behavior data and links variables to the churn effect. The number of times statements have been downloaded, the occurrence of users reading account policies, unsubscription from newsletters and mailings, and other signs of churn behavior can all be used as explainer variables. Banks can better serve their customers by adapting their offerings and prices as a result of processing their data.

The model is based on historical data of clients who have canceled their policies as well as those who have stayed after considering leaving the institution.

For this specific industry, a study article on customer attrition prediction for the banking industry demonstrated the value of consumer research against mass marketing:

The mass marketing approach cannot succeed in the diversity of consumer business today. Customer value analysis along with customer churn predictions will help marketing programs target more specific groups of customers.

9. Augmented research tools

In investment finance, a large portion of time is spent doing research. New machine learning models increase the available data around given trade ideas.

Sentiment analysis can be used to perform due diligence on businesses and executives. It enables an analyst to see the tone/mood of vast amounts of text data, such as news or financial reviews, at a glance. It can also reveal how a manager evaluates their company’s performance.

Satellite Image Recognition can provide researchers with access to a large number of real-time data points. Parking lot traffic at specific locations (such as retail stores) or ship traffic in the ocean are examples of this. The model and the analyst can infer business insights from this data, such as the frequency of shopping at specific stores of the above-mentioned retailers, the flow of shipments, and routes, among other things.

Advanced NLP techniques can help a researcher analyze a company financial reports quickly. Pulling out key topics that are of most interest to the firm.

Financial statements can also be formatted and standardized using other data science techniques.

10. Valuation Models

Valuation models are commonly used in investment and banking applications.

Using data points around the asset and past instances, the model can swiftly calculate the asset’s value. These are the same data points that a human would use to value an asset (for example, the artist of a painting), but the model learns which weights to assign to each data point based on previous data.

This model has traditionally been used in the real estate industry, where the algorithm can be trained using previous sales data. For financial organizations, it can use financial analysis data point, market multiples, economic indicators, growth estimates; all to predict the worth of company/assets.

Investment banking teams employ such models as an internal tool.

Every day, technology advances and this list will continue to grow. For the time being, AI will help finance organizations enhance their operations, marketing, sales, customer experience, revenues, and overall transaction quality.

The Future of Artificial Intelligence in Financial Services

Experts predict that as AI becomes more prevalent in finance, its application will spread across additional industries, resulting in increased crossovers and, as a result, tensions, particularly in the area of data access.

The epidemic has expedited the shift away from physical to digital communication, which has impacted the whole banking industry. 

However, how much financial services companies invest in upskilling their workforce will ultimately determine how much AI is used in the industry. According to Spencer Tuttle, SVP WW Sales at ThoughtSpot, the AI and search-driven analytics provider, this upskilling is required to get real value from democratising insights.

“According to the data, the industry is halfway through upskilling its employees, with 49 percent of respondents reporting that training initiatives for employees to better understand AI are currently in place.” 

“An end objective is to be able to react at the speed of thought to changing situations, markets, and information: Making the best use of time because getting to understanding has never been a quick process in the history of business intelligence,” he continues.

The future of AI chatbots in financial services

Chatbots, according to Juniper Research, are the future of fintech customer service because they can handle a wide range of customer demands that can be handled by AI technology rather than human call handlers, who can be deployed to deal with more complex enquiries. Research shows that: 

  • Successful banking-related chatbot interactions will grow 3,1505% between 2019-2023. 
  • 826 million hours will be saved by banks through chatbot interactions in 2023. 
  • 79% of successful chatbot interactions will be through mobile banking apps in 2023.

FutureWorkForce’s Director of Automation, Dan Johnson, predicts big developments in four areas over the next five years:

Process control and optimization (PCO) using process mining and management tools will assist businesses improve the efficiency, speed, and overall productivity of their business processes.

Virtual or Robo assistant chatbots driven by AI and ML will answer in seconds, improving customer experience. With the market’s increasing rivalry, timely client involvement will be essential.

Credit scoring: The bulk of credit scoring systems in use today are obsolete. Their decisions are based on demographics, age, marital status, and possible preferences of a fictitious client base. To reduce attrition and improve customer experience, AI and machine learning will be used for decision making, compliance, and proactive customer marketing..

Insecurity, the rising usage of AI by cyber defense tech businesses, proactive mechanisms for fighting off attacks and securing important data from hackers will be available.

2 thoughts on “AI In Fintech: Use Cases, Applications And Future Of AI In Financial Services”

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