In 1950, when Alan Turing questioned whether machines could think, no one knew how much capability artificial intelligence would have more than 70 years later. This question alone gave rise to complex concepts like machine learning (ML), robotics, deep learning, expert systems, neural networks, and all of what we now know as artificial intelligence (AI).
Over the years, AI has found its way into every single industry, disrupting the norm and status quo. The banking and finance industry hasn’t been left out and below are some of the ways AI in finance is redesigning the industry.
How Is AI Impacting Finance?
As AI establishes its presence in the world of finance, it brings with it numerous impacts—both beneficial and detrimental.
According to Intone Networks, AI has positively transformed the banking and financial services industry by:
- Minimizing operational costs
- Improving customer support
- Improving risk management
- Automating routine processes
- Increasing service speed
- Improving data processing accuracy and speed
AI is set to save the banking industry approximately $1 trillion by 2030 and $447 billion by 2023, as discussed by Business Insider.
Despite all the good that AI brings to the world of finance, it also has some negative impacts. According to Forbes, AI exposes the industry to risks such as cyberattacks, credit risk miscalculations, and the much dreaded wiping out of human capital and employment—which will be demystified later in this article. It is, however, undeniable that the beneficial impacts of AI outweigh the risks.
How Is AI Being Used in Finance?
Artificial intelligence is changing the way we deal with money in finance. From credit decisions to quantitative trading and fraud detection and prevention, there are plenty of use cases for AI in assisting the financial and banking industry. Let’s explore 11 different ways AI is being employed in finance and banking.
1. Investment Management
As AI deepens its roots in the financial industry, it is becoming more and more crucial for investors and asset managers to find a way to integrate it into their investment processes. With AI, investment managers can analyze technical and fundamental datasets, build predictive models, and generate investment ideas faster and in real-time. AI also makes investment a passive process that requires minimal supervision, which reduces costs while bringing in money at the same time.
AI can also be used in:
- Portfolio management
- Risk management
- Compliance oversight
- Automated analytics
- Automation of office tasks
As revealed by Deloitte, Man Group’s hedge fund—which manages more than $12 billion using AI—has quintupled the fund since 2014.
The potential of AI in investment management is yet to be fully tapped. The Alan Turing Institute reported that around 9% of hedge funds utilize AI and machine learning for investment and asset management. This number is, however, expected to increase over the years.
2. Risk Assessment
An increase in the amount of financial data being processed by organizations calls for an increase in the accuracy in which this data is being processed. This, however, is near impossible for human hands and brains. This creates leeway for fraud risk, which is ravaging most financial services companies these days. A recent PwC report revealed that 47% of companies investigated were victims of fraud, with an average of six incidents reported per company.
To detect even the slightest risk, a company needs to analyze every single transaction in large data sets. This is where AI comes in. With the help of AI auditing software, financial institutions can now mine through mountains of data in a short time and, with precision, flag out anomalies that aid in risk assessment and mitigation.
Since AI eases auditing work, financial institutions no longer have to conduct quarterly or yearly audits. They can have the AI running monthly analysis instead. Continuous auditing enables companies to pinpoint and mitigate risks as soon as they appear.
3. Credit Evaluation
To determine whether someone is eligible for credit, financial institutions conduct a credit evaluation process. This process involves:
- Obtaining relevant information about the applicant
- Analyzing the collected data to determine the applicant’s creditworthiness
- Deciding whether to extend credit to the applicant
- Deciding the amount credited
This procedure is constrained by both time and cost. Businesses run the risk of losing a potential customer if the credit request takes too long. They also run the risk of losing their money if the evaluation process is done incorrectly.
With AI credit evaluation, however, such risks are rendered powerless. Credit evaluation is based on a lot of data, including credit card history, payment history, the amount owed, and length of credit history.
As much as AI helps in combing through such a bulk of data quickly, it possesses the ability to enhance the evaluation process significantly. Credit evaluation based on history is a barrier to new customers even if they are creditworthy.
AI credit assessment can evaluate credit scores based on both historic and forecast data. This way, new customers, students, and startup founders can overcome the historical-based credit barrier. AI credit evaluation stands to benefit financial institutions by bringing in more customers while reducing risks. Customers, on the other hand, get better and unbiased access to credit services.
4. Securities Trading
AI trading solutions allow computers to:
- Think independently
- Analyze historical market data
- Develop strategies based on that data
- Make trading decisions
- Manage risk
Some studies reveal that AI-led hedge funds have the edge over others. These funds have generated an average return of 34% in three years compared with 12% for the global hedge fund industry.
5. Risk Management
Financial institutions always face risks, including:
- Identity theft
- Credit risk
- Fraud risk
- Underwriting risk
AI mitigates such risks using advanced analytics and predictive analytics to spot specific patterns and reduce risk when these patterns are violated.
A good example would be in the case of financial identity theft. The AI would notice anomalies in purchase behavior and block the card before further damage ensues. AI can also predict the probability of the consumer defaulting and prevent the credit from being extended, thus saving the bank from a bad loan.
Infopulse pinpointed the three areas of risk management that would most benefit from AI:
- Early warning systems—for credit risk management
- Stress testing—for market and economic risk management
- Data quality—for fraud risk management
6. Fraud Detection
Fraud has long been a menace to the financial and banking industry. According to Crowe, fraud chews up $5 trillion from the global economy each year, and that number continues to rise. Traditional fraud prevention methods are giving way to machine learning, which showcases greater efficiency at the job.
Modern fraud detection systems can continuously learn from previous fraud tendencies and spot them in future transactions. Since they also ease the data analysis work, fraud analysts are more efficient and can focus on what matters.
Highmark Inc. has saved over $850 million in fraud prevention in the last five years, thanks to AI fraud detection and prevention. Now imagine how much the global economy would save with AI.
7. Personalized Banking
In the current business world, customer satisfaction is key to building long-term relationships and customer loyalty. According to a study by Accenture, of 47,000 banking and insurance consumers surveyed, over 80% would be willing to share their personal data in exchange for personalized services.
To match these demands, banks and other financial institutions should utilize the power of AI for their competitive advantage. Tools such as chatbots and predictive personalization AI help in:
- Simplifying interactions with the consumer
- Personalized mobile banking
- Advising the consumer on their spending and savings
- Protecting against fraud
- Sending personalized promotional messages
8. Debt Management
Debt collection and management continue to be an uphill task for many businesses. CNBC revealed that the average debt of an American citizen stands at $90,450, with the amount increasing annually.
Thanks to AI, though, debt collection doesn’t have to be a complicated, unproductive, and old-fashioned process. Companies can now use behavioral science, data analytics, and machine learning to automate, ease and make this process effective while maintaining good consumer relations.
As outlined by Receeve, AI will reshape data collection by:
- Utilizing statistics and data to improve repayment rates
- Using behavioral science to develop debt collection strategies for individual customers
- Automating the payment process
- Using AI for A/B testing
9. Customer Service
Maintaining good customer relations is pivotal to the success of every business. To ensure optimal customer satisfaction, vendors ought to employ AI customer service tools.
Interestingly, customer service is the second most common use of AI. The field was also predicted to receive the most AI investments in 2020, amounting to $4.5 billion. The use of AI in banking customer service is expected to automate 90% of customer interactions through chatbots by 2022, according to the 2019 Chatbot Report.
Some of the ways AI is being used in customer service is through tools like:
- Email bots
- Face and voice recognition
10. Compliance Oversight
Compliance plays a significant role in the financial sector by ensuring businesses follow both internal and external rules. This ensures market fairness, efficiency, and transparency and protects financial institutions from the risks involved in violating these rules.
In the past, compliance officers were tasked with digging through various communication channels, searching for anything unethical and/or unlawful. This work was costly, time-consuming, and prone to many errors and risks that arose due to these errors.
However, with AI, compliance oversight has been simplified. AI enables compliance teams to sift through multiple sets of data in the shortest time possible with superb accuracy.
The beauty of AI is that, through ML, compliance isn’t only based on a specific set of rules but also on anything new that is outside the norm. In case of a new anomaly, AI takes in the new rarity and improves the whole compliance process. This makes compliance a self-sustaining system in an organization.
A recent survey conducted by NICE Actimize revealed that 89% of the participating companies confirmed that they were on the path to using AI for compliance purposes. Although reports have been made of invasions of private data, discrimination, and bias—which led to the development of the European Union’s AI Act — it is time to embrace AI as the new compliance officer.
11. Market Research
Research is a crucial part of the financial industry—it is the engine that drives decision-making. Before a hedge fund invests in a particular stock, market research must be conducted. Before a bank or insurance company launches a new product or service, market research must be undertaken.
Market research involves loads of data that is analyzed and translated into insights, forecasts, and trends. Forbes reports that traditional market research is not only costly and slow but is also a closed resource that blocks the advancement of knowledge. The cost of conducting traditional market research is, on average, approximately $30,000.
With the advent of AI in market research, financial institutions can now sift through billions of datasets and develop strategies, products, or services at a significantly lower cost and in less time than traditional market research.
For instance, traders can test the viability and profitability of their strategies based on historical market data, giving them the ability to forecast their trading performance before committing real money. All within very short timeframes—and all thanks to AI.
Which Industries Are Benefiting the Most When it Comes to AI in Finance?
Within the financial industry, some sectors are profiting more from AI in finance in comparison to others, and these are:
There’s no denying that banks have benefited the most from AI in the financial sector. Through AI, banks have been able to:
- Reduce operational costs through Robotic Process Automation
- Improve customer relationship management
- Enhance fraud detection and regulatory compliance
- Improve credit evaluation processes
- Automate investment processes
- Improve debt management
Retail, business, and investment banks make up the lion’s share of the finance sector. This is why they are also the biggest beneficiaries of AI in finance.
Funds and Investments
The other significant beneficiaries of AI in finance are hedge funds and investment firms. These firms have benefited from AI in the following ways:
- Automated trading systems
- Instant, data-backed high-frequency trading
- Enhanced data analysis
- Improved risk management
- Enhanced ability to generate alpha
Last but not least, insurance companies have benefited from AI in the following manner:
- Data-based individual pricing for consumers
- Automation of claim management
- Automation of payouts
- Enhanced fraud detection
Pros and Cons of AI in Finance
AI brings with it its fair share of benefits and drawbacks, especially for the financial world.
Some of the pros of AI in finance are:
- Faster and more accurate analysis of loads of data
- Enhanced risk assessment and management
- Improved fraud detection and prevention
- Elimination of human error
- Improved data quality
- Better decision making
Some of the cons of AI in finance are:
- High implementation and maintenance costs
- Inability to make judgment calls
- Kills human creativity
Future of AI in Finance
AI in finance is still in its developmental stages, and it continues to proliferate. Despite the disruptive innovations that have stemmed from it, it’s undeniable that more incremental and architectural innovations will crop up in financial AI in the coming years.
There isn’t a moment in history where humans have stopped innovating. Based on the continuous improvement of AI, it is only wise to agree that AI will further deepen its roots within the world of finance and continue to find more uses.
Some AI improvements that the finance sector might see soon include:
- Increased data sharing between financial institutions
- Improvement in Natural Language Processing
- Improved credit access
- Elimination of algorithm bias
According to McKinsey, AI is set to generate value above $1 trillion annually in the banking industry.
AI in Finance FAQs
Will AI Replace Humans?
Most definitely not. The truth of the matter, however, is that AI will replace redundant and menial jobs. Some careers and jobs are bound to be wiped out by AI.
However, a recent report by WEF reveals that AI will eliminate 85 million jobs by 2025 but will also create 97 million new jobs at the same time. Therefore, this calls for reskilling and upskilling to avoid being rendered obsolete by AI and to prepare for the new era of work.
Is AI in Finance Safe?
Although there are some risks involving ethics, data protection, regulations, security, governance, and transparency, financial institutions have to minimize such risks and mitigate the impact if they occur. Thus, it is wise enough to conclude that AI in finance is safe.
How Accurate Is the Application of AI in Banking?
AI and ML technologies are built upon data that uses algorithms to analyze data and make predictions based on that information. Even though it is difficult to achieve 100% accuracy, AI in banking provides much higher accuracy than traditional banking can.
The beauty of it all is that AI is always self-improving and self-learning—so its accuracy and efficiency levels are constantly increasing. AI in banking enables more accurate fraud detection, bookkeeping, credit evaluation, and risk assessment than traditional banking can.
Is machine learning engineering the right career for you?
Knowing machine learning and deep learning concepts is important—but not enough to get you hired. According to hiring managers, most job seekers lack the engineering skills to perform the job. This is why more than 50% of Springboard’s Machine Learning Career Track curriculum is focused on production engineering skills. In this course, you’ll design a machine learning/deep learning system, build a prototype, and deploy a running application that can be accessed via API or web service. No other bootcamp does this.
Our machine learning training will teach you linear and logistical regression, anomaly detection, cleaning, and transforming data. We’ll also teach you the most in-demand ML models and algorithms you’ll need to know to succeed. For each model, you will learn how it works conceptually first, then the applied mathematics necessary to implement it, and finally learn to test and train them.
Find out if you’re eligible for Springboard’s Machine Learning Career Track.