AI in Finance: How It's Used, Challenges and Expectations

9 mins read

Artificial intelligence has entered many different industries, and the finance industry is no exception. AI is used in a variety of ways in finance, from helping banks detect fraudulent activity to making it easier for customers to manage their money. 

 

In this blog post, we will take a closer look at how AI is used in finance, what roadblocks it faces and what to expect in the future. All illustrations are AI-generated from text-to-image command prompts like “robot banker,” “Matrix bank,” etc.

 

Management of claims and AI-based fraud detection 

One way AI is used in finance is through claims management and fraud detection. Banks rely on AI to help them quickly and accurately process customer claims. AI can also be used to detect fraudulent activity, such as fraudulent claims or credit card usage. By using AI, banks are able to save time and money while still providing excellent customer service.

 

The claim processors who manually review the thousands of claims that come in each day have a difficult and tiresome task at hand. It would be impossible for human workers to spot trends and swiftly ascertain whether a claim is perhaps fraudulent or not given the vast amount of insurance claims generated. 

 

AI models are assisting insurance companies fight fraudulent claims and improve their detection abilities by utilizing AI in finance and its predictive analytics capabilities, as well as by continuously monitoring developing data and claims filed at all times of the day. AI may save businesses time and money for claims fraud detection by utilizing natural language processing to examine transcripts and machine learning models to find patterns and flag anomalous claims for further evaluation.

 

Quantitative trading AI

Another way AI is used in finance is through quantitative trading. Quantitative trading is a type of trading that uses mathematical models to make decisions. AI can be used to help identify trends and make predictions about future market movements. This information can then be used to make buy, sell, hold and all sorts of other trading decisions.

 

Today’s investors are playing with very high stakes due to rising market volatility and increased competition. Additionally, due to the constant flow of information, it is impossible for traders to use conventional methods to stay completely up to date on the most recent markets and make wise investment selections. Algorithmic trading powered by AI can therefore aid investors in carrying out lucrative trades more quickly. 

 

Investors can better take advantage of arbitrage opportunities using AI-powered quantitative trading because AI algorithms and models are adept at spotting and acting upon such opportunities. The difference between a lucrative and unprofitable trade can occasionally change in milliseconds in today’s fast-moving markets, making it extremely difficult to execute through human efforts alone. AI models are increasingly being utilized to help investors stay competitive, even if traders still play a key role in the investment process.

 

 

Predictive analysis

AI is also being used for predictive analysis. Predictive analysis is a type of data analysis that uses historical data to make predictions about future events. AI can be used to help identify patterns and trends in data, which can then be used to make predictions about what might happen in the future. This information can be used by financial institutions to make decisions about where to invest their money.

 

The ability to predict the future is something that all financial services organizations wish they had. With stronger AI capabilities on the horizon, they are one step closer to achieving this goal. All financial services organizations may benefit from predictive analysis, which aids in improved risk management and more informed credit choices. 

 

Financial AI models operate 24 hours a day, seven days a week to enable real-time monitoring of client actions and new data in order to deliver actionable insights about likely future consumer behaviors or trends. 

 

AI models and algorithms may discover patterns and predict future actions and occurrences by examining past data. Predictive analysis increases business intelligence for these companies and enables them to make better decisions, which has significant ramifications for fraud detection and establishing the creditworthiness of customers.

 

Credit scoring

AI is also being used for credit scoring. Credit scoring is a way of assessing an individual’s creditworthiness. AI can be used to help assess an individual’s ability to repay a loan or credit card debt. This information can then be used by banks and other financial institutions when making lending decisions.

 

A credit score is the key indicator for financial organizations of an individual’s creditworthiness. As enterprises must maintain a healthy risk profile for their portfolios, the accuracy of a credit score is of the highest significance. While a standard credit score isn’t constantly updated with the most current information, the use of AI in finance may help banks acquire a real-time and more complete understanding of a customer’s creditworthiness. 

 

The accuracy and frequency with which AI algorithms can determine a customer’s credit score enables banks to make better loan choices. AI algorithms may examine past data on an applicant’s financial history, prior loan applications, existing debt, and other factors to provide an accurate depiction of how a specific client affects the firm’s overall risk profile. 

 

Applicants for loans may also profit from the growing usage of AI in the financial sector. With artificial intelligence, banks can analyse more data and information than ever before to create a more comprehensive credit history and creditworthiness picture. Customers whose credit ratings rejected them for a loan may have a greater chance of obtaining one if they undergo a more rigorous screening procedure. This strategy is facilitated in part by AI models that can examine non-traditional data, such as work history or spending trends.

 

 

Loan underwriting

AI is also being used for loan underwriting. Loan underwriting is the process of assessing a loan applicant’s ability to repay a loan. AI can be used to help assess an individual’s income, employment history, and credit history. This information can then be used by financial institutions to make decisions about whether or not to approve a loan.

 

Conventionally a labor-intensive and time-consuming operation, bank loan underwriting is not a particularly efficient procedure. To do thorough due diligence and maintain a healthy risk profile, banks must evaluate applicants’ income, assets, property ownership, and existing debt before accepting their loan applications. This procedure entails sifting through many papers, records, and data from both internal and external sources, which consumes significant time. 

 

The underwriting of loans by banks has grown more precise and efficient with the use of AI. AI is faster than humans at digesting vast amounts of data. In addition, due diligence and risk management are often sophisticated procedures that aid lenders in determining whether a loan will be successful or fail. By automating and streamlining loan underwriting, AI enables banks to save significant time and resources.

 

 

As you can see, AI is being used in a variety of ways in the finance industry. It is helping banks save time and money while still providing excellent customer service. In the future, we expect AI to become even more prevalent in finance as it continues to evolve and become more sophisticated. However, there are also obstacles to AI adoption that we’ll address below.

 

AI Adoption Roadblocks 

Business leaders may be overwhelmed by the process of integrating AI into their organization due to the demand for storage and the shortage of competent staff. Additionally, given the complexity of AI and machine learning, it can be challenging for customers and company executives to completely embrace AI integration. Additional obstacles can include a lack of a clear plan for how the AI model will work or skepticism about the model’s recommendations within the organization.

 

Fear 

Above all, AI models are intricate and dynamically adjust to the entry of new data. This brings up the “black box” issue, or the uncertainty around how AI operates and generates its recommendations. Lack of understanding of how AI makes decisions makes it difficult to develop trust in the system, which causes customers and corporate executives to be afraid of the unknown. 

 

There is no shortage of hesitation among business leaders and customers about the increased use of AI across the financial services industry, whether it be due to concerns about their jobs being replaced by AI systems or the unease about potential erroneous recommendations and bias from AI models over human decision-making. Even though the adoption of AI isn’t about to slow down, once these concerns are allayed, further integration might even accelerate. 

 

Financial services companies can overcome this hurdle by educating staff members and clients about the issues the AI model is trained to address and by being able to support its suggestions. People will have less anxiety over AI’s increased integration into the sector as they become more accustomed to it.

business maturity 

 

Companies of any size or stage can successfully integrate AI into their operations, however a more sophisticated or established organization may require more labor. An established company’s present technology may not be able to support advanced storage architecture, continuing maintenance, and updates, which could result in additional costs. 

 

It is feasible that the time and money involved will be sufficient to take away from core operations and competitiveness in the short term because pursuing AI integration can be a considerable task for an established organization. Businesses may not be able to see past the initial AI barriers and forego further integration, despite the fact that adoption of AI is essential to their survival in the modern economy. 

 

The firm can decide to apply AI in stages, assuring the integration’s success over time without imposing a huge upfront financial burden. This will help to reduce the possible expenses of altering the present architecture of an established organization. AI can be successfully integrated into an established business by starting with simpler activities and progressively moving toward larger use cases. 

 

 

Dearth of talent 

 

Many models require professional employees to maintain the system and aid in the daily interpretation of the insights gathered due to the technological complexity of AI and machine learning. Even if the use of AI is increasing, there is still a lack of skilled professionals in all fields. At least 23% of mature AI adopters report a major or extreme AI skills gap, which is a barrier to further integration. 

 

Given their high demand today, many financial services companies might not even be able to afford to recruit a highly sought-after data analyst or computer programmer. According to a recent study, 56% of business and IT leaders think that as AI use rises, personnel in both new and existing jobs will need to pick up new skills to work with AI. Therefore, greater AI adoption is expected to have an effect on workers at all levels, whether or not they are working on the AI model right now. 

 

More people will be trained for employment in AI as the market expands and demand for these workers remains high throughout time as AI adoption picks up across industries. Despite the fact that there is still a pressing need for competent professionals in AI, this will help to reduce the skills deficit in the years and decades to come. Because of this, businesses may be able to identify current workers who have an interest in AI and are eager to continue their education to get more knowledge in the area and aid their company’s adoption of the technology. 

 

No roadmap 

The selection of the best application area for AI models is a complex process that calls for the company to think about its long-term goals and the actual applications of AI in the industry. Because there are different needs for every organization in the financial services industry, each one will use AI in a different way, in line with its overarching objective. 

 

Financial services companies want to make sure they’re receiving the most value out of their investment in AI, therefore businesses must define a clear plan for installation, expectations, and continuing maintenance in order to achieve successful AI integration. The continued success of AI integration and ROI optimization depends on having a clear understanding of what the company wants the AI model to achieve and making sure that it is in line with those goals. 

 

Resistance within the company’s culture

It may be difficult for an established company to persuade long-tenured personnel to abandon the systems and procedures they have been using for years in favor of a computer-based program. Thus, for AI integration attempts to be effective, financial services organizations need to have company-wide support. 

 

In any industry, change is difficult, but it is especially challenging in the financial services sector where technology adoption has historically been sluggish. Additionally, some workers might feel frightened by the increased use of AI and worry that their jobs could be taken over by such programs, which could cause resistance to further integration. 

 

Although it can be obvious to investors and technology experts why a firm has to implement AI, employees of all levels and skill sets might not agree. As a result, for AI to be successfully integrated into the firm, all stakeholders must be aware of the value that it adds. Company executives can also allay concerns by emphasizing that AI won’t replace workers; rather, it will make their jobs easier and free them up to concentrate on more interesting and less repetitive duties. 

 

Conclusion

Although financial services companies are starting to adopt AI, there are still some challenges that need to be overcome. These include the skills gap, resistance within the company’s culture, and the selection of the best application area for AI models. However, if businesses can define a clear plan for installation, expectations, and continuing maintenance, they will be able to achieve successful AI integration. Additionally, it is important for all stakeholders to be aware of the value that AI adds in order for it to be successfully integrated into an organization. 

 

 

 

 

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