10 applications of artificial intelligence in financial technology

liu, tempo Date: 2021-07-21 10:10:56 From:ozmca.com
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The financial industry pays attention to technological progress with great interest. Big banks like JPMorgan Chase have become early adopters of disruptive technologies like Blockchain.

Artificial Intelligence (AI) is a paradigm shifting technology that can seamlessly change the way we live, move, interact, and shop.

Fin-tech is the general term for the use of cutting-edge technology in the financial industry.

Digital financial coach

In this article, we will introduce ten applications of artificial intelligence and the breakdown of the technology.

1. Digital financial coach/consultant

Trading robots are one of the most popular use cases in artificial intelligence, probably because of the wide range of applications: in all industries, on multiple levels.

In terms of finance, trading robots can be used to provide users with financial counseling/consulting services.

Think of them as digital assistants to help users browse their financial plans, save and spend. This service increases user engagement and improves the overall experience of the financial products that users interact with them.

Digital assistant can use natural language processing building (NLP), natural language processing is a machine learning model, it can format the data to process human language. A layer of product recommendation model can be added to allow assistants to recommend products/services based on transactions between algorithms and human users.
Sun Life has deployed an example of this application, which it created to help users get benefits and pensions by allowing users to maintain their insurance plans. The assistant sends user reminders based on user data, such as “health benefits that are about to expire” or “your child will receive benefits soon”.

Digital assistants can also be used in other financial-related scenarios: dividend management, term renewal, transaction limits approaching or checking cash notifications.

2. Transaction search and visualization

Chatbots can also be used in banking, focusing on search tasks.

The administrator accesses the user’s transaction data (bank transaction) with the robot, and uses NLP to detect the meaning of the request sent by the user (search query). The request may be related to balance inquiry, consumption habits, general account information, etc. Then, the robot processes the request and displays the result.

Bank of America uses such robots (called) as digital financial assistants for its customer base. Artificial intelligence robots were quickly adopted.

The bot provides user-friendly transaction search, enabling users to search for specific transactions of specific merchants in their historical data, avoiding the hassle of finding these transactions in each bank statement. The robot also calculates the total amount of credit and debt, which is a task that users must complete on their own on the calculator.

3. Customer risk warning

A key part of the work of banks and insurance companies is to analyze customers based on their risk scores.

AI is a good tool because it can automatically classify customers from low to high according to their risk profile.

Based on the classification work, the consultant can decide to associate financial products for each risk profile and provide it to the customer in an automated manner.

For this use case, classification models such as XGBoost or Artificial Neural Networks (ANN) will be trained based on historical customer data and pre-labeled data provided by consultants to eliminate data-induced deviations.

4. Underwriting, pricing and credit risk assessment

Insurance companies provide underwriting services, mainly for loans and investments.

The artificial intelligence model can provide an instant assessment of the customer’s credit risk, and then allow the consultant to formulate the most suitable quotation.

Using AI for underwriting services can increase the efficiency of proposals and improve customer experience because it can speed up the process and turnaround time of such operations.

Canadian financial services group Manulife is the first company in the country to use artificial intelligence as its underwriting service.

Insurance companies use a specific artificial intelligence decision-making algorithm (AIDA), which is trained through previous underwriting methods and expenditures, and can have different classification processes, such as large loss payments or prices.

The application of this method is not only for insurance; it can also be used for loan credit scoring.

5. Automatic claim process

The insurance industry as we know it works in a standard process: customers order insurance and they pay for it. If the customer has a problem (illness in health insurance, car accident in car insurance, water damage in housing insurance), she needs to activate her insurance by filing a claim. This process is usually long and complicated.

Trading robots can transform the user experience into a more enjoyable process.

Through image recognition, fraud detection and payment prediction functions have been enhanced, and the entire user journey has been upgraded-reducing friction, reducing company costs, reducing operational tasks (calls, background checks), and reducing errors. The whole process takes less time and becomes a seamless experience for customers and insurance company employees.

What the robot does is responsible for the entire cycle: it guides the customer through the entire process step by step in the form of dialogue.

Swishbot is a trading bot we built from the ground up and can be used by insurance companies for their customers.

It asks for the damaged video or photo and uploads it to the database. It receives all the information needed to process the claim. The bot can then run the application through fraud detection methods to find abnormal and non-compliant data.

Then it goes to the adjustment model, where it provides a series of values for the payment. Once all the data is set, manual intervention can be included for audit purposes. At this point, the robot can calculate and propose the payment amount according to the trained payment prediction model.

The application is a three-in-one machine learning solution with the potential to alleviate high pain points in the industry.

6. Contract analyzer

Contract analysis is a repetitive internal task in the financial industry. Managers and consultants can delegate this routine task to the machine learning model.

Optical character recognition (OCR) can be used to digitize hardcopy documents. Then, the NLP model with hierarchical business logic can interpret, record and correct contracts at high speed.

Business logic is a conditional format similar to that found on Microsoft Excel. Formulas can be added to the model, such as “If this box is checked, it should be blank.” Model training can be performed on existing contracts and learn how to use such content to operate.

In this case, due to the repeatability of the contract, the accuracy of the model results is very high.

JPMorgan Chase has already taken advantage of the power of this AI application.

These solutions support contract-related analysis, and blockchain-based smart contracts are being more widely adopted, which is a paradigm shift and upgrade to contract management.

7. Churn prediction

Churn rate (or churn rate) is a key performance indicator for all industries and companies. Companies need to retain customers, and in doing so, predicting upcoming churn is very helpful in taking preventive measures.

AI can support managers in this task by providing a prioritized list of customers who show signs of considering canceling their policies. The manager can then process this list accordingly: provide a higher level of service or improve the product.
In this case, the model is based on the churn effect of customer behavior data, based on customer behavior data. The explanatory variables can be the number of downloads, the occurrence of the user’s account reading strategy, the unsubscription of newsletters and emails, and other indicators of churn behavior. By processing consumer data, banks can better serve them by adopting their products and pricing.

The model used is a trained classification of the historical data of customers who have cancelled their policies and the historical data of others left after considering leaving the organization.

A prediction of customer churn for the banking industry shows the importance of consumers to research quality marketing in this particular industry:

Mass marketing methods cannot succeed in the diversity of today’s consumer business. Customer value analysis and customer churn prediction will help marketing plans to target more specific customer groups.

8. Algorithmic trading-the most advanced ML you will never see.

Most algorithmic trading applications take place behind closed doors by investment banks or hedge funds.

Trade frequently, analyze data and make decisions quickly. Machine learning algorithms are good at analyzing data, regardless of its size and density.

The only prerequisite is to have enough data to train the model, which is the richness of the transaction (market data, current and historical).

The algorithm detects patterns that are usually difficult to be discovered by humans, it reacts faster than human traders, and it can automatically execute transactions based on insights derived from data.

This model can be used by market makers looking for short-term transactions based on rapid price changes. These operations are time sensitive, and the model provides the required speed.

An example of this is the price movement of trading individual stocks with the S&P 500 Index, which is a known leading indicator (ie, stocks follow the index). The algorithm obtains price changes from the index and predicts the corresponding movement in a single stock (for example: Apple). Then immediately buy (or sell) the stock and place the limit order at the predicted level, hoping that the stock will reach that price.

9. Enhanced research tools

In the field of investment finance, most of the time is spent on research. The new machine learning model increases the available data around a given trade idea.

Sentiment analysis can be used for due diligence on companies and managers. It allows analysts to see the tone/emotion of large amounts of text data (such as news or financial reviews) at a glance. It can also provide insights on how managers reflect their company’s performance.

Satellite image recognition can give researchers insight into many real-time data points. Examples of this are parking lot traffic in a specific location, such as a retail store, or freighter traffic in the ocean. Based on this data, models and analysts can obtain business insights, such as the shopping frequency of the above-mentioned retailer’s specific store, shipping process, route, etc.

Advanced NLP technology can help researchers quickly analyze the company’s financial reports. Pull out the key topics that the company is most interested in.

Other data science techniques can also format and standardize financial statements.

10. Valuation model

Valuation models are usually investment and banking applications.

The model can quickly calculate asset valuations using data points around the asset and historical examples. These data points are used by humans to evaluate the content of the asset (for example: the creator of the painting), but the model uses historical data to learn the weight assigned to each data point.

This model is traditionally used in real estate, where algorithms can be trained on previous sales transactions. For financial companies, it can use financial analysis data points, market multiples, economic indicators, growth forecasts; all of these can predict the value of the company/asset.

These models are used as internal tools by the investment banking team.

This is an overview of the application of artificial intelligence to financial technology. The technology is growing every day, and this list will expand. Currently, financial companies that adopt artificial intelligence will improve their operations, marketing, sales, customer experience, revenue, and overall transaction quality.

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