Artificial Intelligence in Financial Services: Applications & Benefits

Artificial Intelligence in Financial Services

Since Artificial Intelligence was first introduced, it’s been increasing value across different industries, and banking and finance are among them. The hospitality industry is, of course, always changing, but following the digital transformation for hotels of the last few years and the adaptations required across all sectors, 2023 is looking likely to be one of significant shifts. Partnering with a reliable custom software development company, financial organizations have already been implementing AI-driven solutions to automate processes, mitigate financial risks, customize the user experience, etc.

The innovative technology has been revolutionizing how organizations in the finance industry handle key operations and interact with customers. The benefits of implementing AI in the sector are monumental. The financial institutions that will employ AI will be able to:

  • enable 24/7 interactions with customers;
  • reduce the number of repetitive tasks;
  • eliminate human errors;
  • reduce operational costs;
  • prevent risks in the finance industry;
  • improve customer experience;
  • enhance financial decision-making based on data analytics insights.

How Is AI Applied in Finance?

Process Automation

Finance automation implies the use of AI-based software to automate repetitive, labor-intensive tasks. In finance, such processes as journal entries, bank reconciliation expense management, credit control, and financial reporting are typically automated.

Robotic Process Automation (RPA) plays a crucial role in finance automation as it boosts efficiency, compliance, and productivity. According to Gartner around 80% of finance leaders have implemented or are planning to implement RPA.

Financial Management

The use of AI and ML technologies can streamline bookkeeping, accounting, and other financial processes within an organization. AI makes these processes more accurate by eliminating human errors through completing simple yet routine tasks. For example, the automation of the financial tasks related to data entry and invoice processing results in greater accuracy.

By taking over time-consuming financial tasks, AI can free up more time for financial experts to focus on more strategic tasks like financial analysis, financial reporting, etc. Another great impact of AI on financial management is the ability to rapidly identify financial insights based on the organization’s financial records. Thanks to the technology, business leaders get access to real-time insights that help them in taking data-driven financial decisions.

Fraud Detection

Frauds are the biggest challenge in the financial sector. 2021 Half Year Fraud Update shows the scale of the problem: in the first half of 2021, criminals stole a total of £753.9 million through fraud, an increase of over a quarter compared to H1 2020. The finance industry applies AI technologies to detect these frauds as early as possible and prevent them. Below are the key examples of how AI is used for fraud detection.

  • Fraudulent transactions. Bank account and credit card details are often stolen by cybercriminals to steal money from customers’ accounts. To prevent this type of fraud, banks now implement ML models that detect suspicious activities and transactions to stop them and alert customers and authorities.
  • Phishing scams. Cybercriminals often send fake emails that look like the ones from banks and other financial organizations and contain malicious links. As per one of the latest reports by a cybersecurity company ProofPoint, there were 9.2 million suspicious emails in 2020. Though banks aren’t involved in scamming prevention, email companies apply ML models to detect phishing emails and alert users so that they don’t click any links in such emails.
  • Surveillance and security of ATMs. Cameras integrated with ATMs are now smart enough to detect suspicious activities and alert authorities about them. Some banks also integrate biometric security features, including facial recognition and fingerprint to prevent fraudulent access even if the mobile device is stolen or lost.

Personalized Banking

Financial organizations have to operate in an extremely competitive environment, so they have to understand their customers better to be able to engage and retain them. The effective application of Artificial Intelligence in finance allows banks to offer enhanced products and services tailored to customer preferences.

Based on customers’ behavior, banks can recognize their needs and offer personalized suggestions of services. For example, AI models can use transactional data to help banks to understand how to improve customer experience and at the same time encourage them to use their credit cards again. If a customer uses a credit card to book a hotel or purchase specific goods, AI can help suggest personalized offers relevant to customers’ needs and linked to their credit cards.

AI also brings efficiency in providing personalized consultative, advisory services. AI-based chatbots are implemented to handle calls with basic and common questions, while customer service specialists can spend more time answering more complex questions by providing customized advice about services.

Risk Management

Financial institutions are vulnerable to fraud due to the information they deal with. AI can significantly streamline the management of such risks. Banks and other financial organizations apply AI in different functional areas.

AI helps manage risk associated with money loaned to banks’ clients. They can fail to repay a loan, and AI can predict these risks. Machine learning algorithms are trained to assess customers’ credit histories to identify their paying capacity. The insights help to reveal vulnerabilities or patterns that can be missed by humans.

Another common example of AI-based risk management is market trading. ML algorithms can analyze huge amounts of money, which will take days if humans perform it. By AI-powered analysis, traders receive insights into optimal price rates, which allows them to achieve high precision with market price forecasting.

What Is the Future of AI in Finance?

The implementation of AI in fintech software development continues to grow, so experts believe AI will spread across different sectors. Juniper Research states that chatbots are the future of financial services as they deal with multiple customers’ requests with the help of AI, allowing human workers to focus on more complicated requests. According to the latest predictions, 47% of organizations will use chatbots for customer services.

The pandemic has accelerated the shift from in-person to digital service delivery. Still, the speed of AI adaptation will significantly depend on how much organizations are ready to invest in its implementation and upskilling of their workforces. Moreover, banks will need employees with skills to apply AI technologies. The research states that 29% of banks report that the lack of skills has been a barrier to transformation, so 37% of banks and credit unions are beginning to invest more heavily in their people.

Finally, AI and big data require powerful data storage capacities, so banks are increasingly turning to the cloud, which allows them to scale easily and at reasonable costs. According to recent reports, global spending on cloud services will surpass $1.3 trillion by 2025, growing at a CAGR of 16.9%.

Therefore, AI will have a profound impact on financial services, helping companies to adapt to growing market demands and customer expectations, while the success of AI implementation will depend on the approach companies will choose.

The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of The World Financial Review.