What are the Top Benefits of Artificial Intelligence for Banks?
Below is a list of the five of the top benefits of artificial intelligence in the finance and banking industry. We also discuss some of the risks and challenges faced by the financial services industry when using artificial intelligence.
- Regulatory Compliance and Fraud Detection
- Improved Investment Evaluation
- Better Customer Experience
- Reduced Operational Costs and Risks
- Improved Loan and Facility Evaluation
1. Regulatory Compliance and Fraud Detection
The banking industry has had a colorful past costing investors millions of dollars. Legislation such as the Sarbanes–Oxley Act of 2002 (SOX) lays out hefty penalties for players caught in violation of the regulations. It is therefore in the best interest of banks and financial organizations to automate compliance where possible.
Using a Decision Management System allows for early fraud detection and comprehensive audit documentation. Third-party auditing exercises can be disruptive to regular operations when employees are called away from their desks to provide missing details or explain entries. With the right software and machine learning, information captured in the system will be accurate, and errors immediately highlighted or disallowed.
As financial institutions increase their vigilance, fraudsters alter their behavior. Since large sum transactions are flagged for investigation, fraudsters have learned to deal in amounts just under the limit of detection. Without proper analysis, criminal activity can go undetected despite meeting the prescribed requirements. This is one area where artificial intelligence is genuinely superior to humans. Artificial intelligence analyzes large amounts of data and picks out suspicious transactions. Manually analyzing such transactions leads to mistakes. Without an AI fraud detection system in place, it’s a field day for criminals to launder money or finance illegal activities.
2. Improved Investment Evaluation
Interest income is only one facet of income generation. As a result, banks are continuously searching for lucrative opportunities to invest and earn a healthy return.
The right investment software can provide investment recommendations that match the risk appetite of these institutions. In addition, they can accurately evaluate client funding proposals, given that industry-specific information is often difficult to understand.
The decision to invest is still in the hands of human analysts. Investment analysis software makes the process easier and accommodates more variables. If the institution has interests outside its national borders, accessing information can be time-consuming. Assessing a new environment can be a challenge, but the right AI software is instrumental in hastening the process.
3. Better Customer Experience
Customers are constantly looking for convenience. For example, the ATM was a success because customers could access a vital service even when banks were closed. That level of convenience has only inspired more innovation. Now, clients can open bank accounts and verify themselves, using their smartphones, from the comfort of the couch.
In the quest for a shorter turnaround time, a decision management system (DMS) can reduce the time it takes to capture Know Your Customer (KYC) information and eliminate errors. In addition, with proper business rules software, business decisions can be implemented and rolled out without lengthy procedures.
New products and seasonal financial offers can be available on time. In addition, new business decisions or changes in tariffs are easily accommodated in the system.
Eligibility is automated meaning, clients who do not qualify are not frustrated by going through an entire process only to be rejected. This kind of technology provides the illusion of a personal touch despite a varied customer base.
Banks can earn the trust and confidence of clients by reducing turnaround time. In addition, DMS software can reduce approval times for facilities.
Sometimes, bank employees open accounts erroneously, leading to restrictions placed on client accounts. That can be very frustrating for a client. Accurately capturing client information and correctly setting up client accounts ensures a smooth experience for your customers.
4. Reduced Operational Costs and Risks
As much as we enjoy human interaction, it has one significant drawback. Errors are common, and they can have serious repercussions. Even when experienced employees are at the helm, the wrong keystroke could expose the institution to liability and cause irreparable reputational damage.
Decision management systems reduce this risk by creating logic flows in data capture and combining predictive and prescriptive techniques to solve business problems.
Let’s use on-boarding as an example. Using DMS, you can set up rules that show the client what types of accounts they can open depending on their bio-data or business information.
If a client is opening an account online, age and source of income can determine the type of account available to them. In that case, underage persons cannot open accounts in their name, and personal savings accounts will not have an overdraft facility. This means that you need fewer customer-facing employees, which reduces your labor cost.
Furthermore, with increased accuracy, the number of people the organization needs to assess transactions and activities is further reduced.
There’s also a benefit to employee wellness. For example, a DMS reduces data entry time, meaning your team can spend more time innovating and focusing on core business tasks.
Despite its advantages, artificial intelligence can’t replace the value of a handshake. However, with the savings derived from investment in AI systems, financial institutions can redirect resources away from data entry to business development.
5. Improved Loan and Facility Evaluation
Using credit scores to evaluate eligibility for financing often relies on outdated information, misclassification, and errors. However, these days there’s so much more information available online that can give a more realistic picture of the person or business under evaluation.
An AI-based system can give approval or rejection recommendations by considering more variables even when the party, whether personal or business, has little documentation.
The tricky bit is that it is not always clear why the software comes up with a particular recommendation. When an application is approved, no one asks any questions. However, when an application is rejected, the institution owes the client an explanation.
Even though systems are designed to be objective, they can demonstrate bias. This is because configurations are only as good as their developers. Fortunately, most of the funding requests received by institutions are similar, and people are aware of institutional bias. As a result, developers are better positioned to key in better variables when designing applications and updates.
