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With artificial intelligence (AI), financial institutions can reduce fraud, provide automated advisory support and improve the customer experience. However, to reap these benefits, there are challenges financial institutions must face.
The term artificial intelligence refers to a machine’s capability to perform “cognitive” functions, such as learning. AI comes in many forms, including generative AI, which is the ability to generate new outputs in the form of text, images and beyond.
Because of the benefits AI offers to financial institutions, many are working to adopt the technology within their organizations. In this guide, we’ll discuss the potential advantages of AI for the financial sector and the associated challenges associated with AI.
Also see: 100+ Top AI Companies 2023
Finance: An Industry in Need of Digital Transformation
The finance industry is one of the most profitable industries in the world. It’s also one of the most volatile, a fact that has been proven in the past few years. Clearly, it’s a prime candidate for digital transformation, including the use of artificial intelligence and generative AI.
The COVID-19 pandemic greatly affected the finance sector. During the pandemic, financial organizations found themselves battling through a recession, rapid stock market declines and more.
Those effects and other more current factors, such as inflation and evolving customer demands, are inspiring financial institutions to look for ways to reduce costs while also boosting efficiency. This is where artificial intelligence and generative AI comes in.
Financial institutions such as commercial banks, insurance companies, investment firms and brokerages are experimenting with AI to determine how it can support the challenging environment that is finance.
With AI, financial institutions may be able to reduce fraud, simplify risk assessment, reduce the number of manual tasks required in day-to-day work and provide more seamless customer experiences.
As a result, these organizations can use AI to reduce costs while also enhancing productivity and protecting their businesses from future monumental events (like pandemics).
See also: Top 9 Generative AI Applications and Tools
AI in Finance: Six Potential Advantages
The future of AI’s use in finance has the potential to be bright. There are various possible advantages, from fraud prevention to automated portfolio management.
1. Fraud Prevention
Machine learning is a powerful subset of AI. Through machine learning, machines can be trained to identify patterns in data. In other words, machines can be taught to “learn.”
The result is machine learning models that can be used to predict outcomes using data, including various risks.
In finance, this form of AI can detect patterns indicative of fraudulent activity such as phishing attacks, and alert users. As a result, banks and other financial institutions can stop fraud before it occurs.
See also: Understanding the Business Costs of Phishing Attacks
2. Improved Customer Experience
Customers have plenty of opinions when it comes to how financial institutions should operate. After all, they’re the ones overseeing their livelihoods.
According to a recent study, 92% of bank customers surveyed consider customer service important when deciding who to open an account with. And almost as many customers, 91%, consider mobile and online access to be important.
To deliver the level of service and access customers expect, banks and other organizations can turn to AI.
For example, some institutions are using chatbots to enable 24/7 access to bank account information. This means customers can do everything from inquiring about their account balances to understanding how much they spent on groceries by speaking with a chatbot.
3. Risk Assessment & Management
Risk assessments are a common occurrence in the finance industry. For example, these assessments are often completed before an investment firm chooses to make an investment. The goal is to determine how much risk is involved in an investment, and whether or not the risk is worth it.
AI can automate risk assessments through machine learning. AI models can be trained to understand certain risk factors and spot patterns during assessments. The AI model can then declare just how risky an investment may be.
This same process is useful for other financial processes, such as credit risk assessments used before extending consumer credit.
4. Automated Advisory Support
AI could be beneficial when it comes to managing financial portfolios and providing advice. For example, financial advisors could use AI tools to detect anomalies and trends in customer financial data. These insights could then be used to adjust portfolios accordingly, averting financial risk.
AI is already a useful tool for portfolio management. Some finance giants are even developing AI tools that will be used to select investments based on data on behalf of their customers.
5. Manual Task & Error Reduction
In finance, one misplaced comma has the potential to be disastrous. Unfortunately, where there are manual tasks, there will be errors. The finance industry still depends on tasks such as manual data entry, which is prone to mistakes.
One of AI’s key benefits is its ability to automate manual tasks, including everything from standard bookkeeping to tax compliance. Automating these tasks eliminates the need for human intervention, which can reduce costly errors.
Also see: Generative AI Companies: Top 12 Leaders
AI in Finance: Five Challenges to Overcome
While AI may enable financial institutions to provide better service and reduce manual tasks, there are still challenges we must meet head-on, including data privacy, bias and quality concerns.
1. Data Privacy & Regulatory Concerns
In the US, specific laws and regulations exist, including the Gramm-Leach-Billey Act that govern the use of consumer data within the finance industry. According to these laws, financial institutions must protect their customers’ personal information.
However, data privacy is a gray area when it comes to using AI. For example, AI tools such as chatbots must often access personal information to answer customer queries.
Does the organization have permission to access and use this information in this way? That’s a question institutions must carefully answer before implementing AI. Otherwise, they may find themselves in serious legal trouble.
For more information, also see: What is Data Governance
2. Bias in Algorithms
Currently, artificial intelligence models must be trained on existing datasets. Unfortunately, it’s common for AI models to undergo training using biased datasets that may underrepresent certain groups of people. This results in AI tools exhibiting bias too.
In finance, bias can result in serious consequences for consumers. For example, credit risk assessments completed using a biased AI model may keep an individual from securing a loan they’re qualified for, simply based on their demographics. And for the customer, this may be detrimental to their livelihood.
3. Data Quality
According to many industry experts, a key factor hindering the adoption of AI is data complexity. Data comes in all shapes and sizes and can be difficult to manage. This is especially true within the finance industry.
The complexities of data can lead to subpar quality. Unfortunately, when AI models are trained on data, data quality is critical. Incorrect data can lead to models that make incorrect assumptions, resulting in organizations making uninformed decisions. And these decisions can directly harm the financial well-being of their customers.
This issue is exacerbated by the lack of data science and AI professionals within organizations. Many companies are finding that a lack of AI skills, expertise and knowledge is a hindrance to AI adoption.
4. Explainability
Another law that affects financial institutions is the Equal Credit Opportunity Act. This law prevents financial institutions from discriminating against credit applicants based on race, color, religion, national origin, sex, and other demographics.
To remain in compliance with this law, financial institutions must be able to explain why a consumer won’t be receiving a loan if denied.
Unfortunately, explainability can be challenging with AI. As AI increases in complexity, so do the steps models take to reach conclusions. And in many cases, it may not be easy to pinpoint why an AI model assigned a high level of risk to one loan application over another.
On a related topic: The AI Market: An Overview
Bottom Line: The Future of AI in Finance
Artificial intelligence is poised to change the finance industry as we know it. For example, AI will automate and improve manual processes such as portfolio management and ensure customers have access to 24/7 service. It may also support labor-intensive tasks such as risk assessment and financial advisory processes.
However, increasing regulatory concerns, a current lack of explainability, and the existence of bias are challenges the industry must face before moving forward. The finance industry should weigh the risks before utilizing artificial intelligence – yet the potential benefits clearly make AI a worthwhile investment.
On a related subject: Algorithms and AI
