9 Applications of AI in Banking and Challenges It Combats

While the banking industry faces many challenges, banks can leverage the benefits of AI and ML-based solutions to upgrade online security and track in-system loopholes in banking and financial sector functionality. Check out 9 AI applications that banking industries use to combat functional challenges. 

1. Competition From Fintechs

The rise of Fintech institutions poses a competitive threat to banking institutions as they target some specific and more profitable areas in financial services. Banks will have to employ an AI-first strategy to safeguard the future of banking and thrive in this competitive environment. 

Banks still following the traditional legacy practices must think and act as tech companies that provide banking services. AI-backed automation in the banking industry can gear the shift toward the digital banking process. AI can automate time-consuming and repetitive tasks to enhance operational efficiency
 

2. Robotic Process Automation

Bank employees deal with large volumes of customer data on a regular basis and are prone to data-related errors. 
Robotic Process Automation has the potential to upgrade the future of banking operations.  It helps automate time-consuming, repetitive processes such as KYC verification, bank account opening, customer service, etc. It also streamlines operational and process efficiency and helps in reducing costs. 
 

3. Managing Risks

One of the major challenges that the banking industry faces is money laundering. The traditional financial guidelines relating to money laundering are constantly evolving, due to which there is a persistent issue of non-compliance which results in monetary and fiscal damages. 

The benefits of AI in banking can be used to employ risk management technologies to help banks determine customer behavior and transaction patterns. It can be utilized to monitor customer activity and closely monitor high-risk customer accounts and transactions by matching customers’ expected monthly turnover to actual monthly transactions to detect odd behavior. 

This helps the banking industry implement controls that prevent losses and fraud. 
 

4. Regulatory Compliances

Meeting regulatory compliances is yet another challenge the banking industry faces caused by a sizeable increase in the regulatory fees related to earnings and credit losses. Banks incur additional costs and risks when they fail to meet regulatory compliance. 
Implementing AI technology can help banking industries collect data, conduct detailed analysis and reporting to standardize their processes, and ensure that the defined procedures and processes are followed correctly and consistently to meet regulatory guidelines. 

5. Changing Customer Demographics And Preferences

Customers are becoming technologically adept, which is why they expect a large degree of customization and personalization in their banking experience. Moreover, the change in customer demographics influences how they perceive technology. Both the older and newer generations have different expectations of digitized banking services. 

To meet both these expectations simultaneously, AI in the banking industry can be used to build a hybrid model that transforms the traditional banking methodology by introducing digital experiences. For instance, banks can include a self-service station at their physical branch to display devices that the customers can use to interact with the bank’s database. 

These devices can also allow customers to schedule an appointment with the bank’s financial advisor if required and access mobile assistants that can offer them behavior-based additional recommendations. 

Implementing AI in the banking industry can allow banks to meet customer demands efficiently. 
 

6. Customer Experience and Retention

We live in an era of readily accessible information. Customers obtaining financial services expect to be provided with personalized experiences at all times through interactive devices. When customers avail a good experience, their rate of turnover increases, which in turn, increases their retention rate. 

AI in banking can be implemented via chatbots to improve customer service delivery. They help enhance customer engagement without any additional costs and resolve customer queries. Many customers prefer virtual assistants for timely query resolution. 

Virtual chatbots or assistants can interact with users to create customer engagement with the help of sentiment analysis to understand emotional cues to help evaluate and solve complex issues. 
 

7. Outdated Mobile Banking Strategy

All banking institutions nowadays have their own custom mobile applications, but it does not inherently mean that the application is being leveraged effectively. Banks can offer customers a feature-rich, fast mobile banking experience with AI-powered technology. 

Banks can utilize AI to introduce features like live chat or digital voice assistants into their banking apps to increase customer engagement and satisfaction. Banks can also introduce mobile payment functionality that enables customers to use their mobile phones as digital wallets to transfer money easily. 
 

8. Cybersecurity Breaches

Banks store sensitive customer data which exposes them to frequent cyber-attacks. Incorporating AI can help banks enhance operational efficiency and security. 

AI-based automation can be used to identify potential security threats at early stages. AI solutions can be deployed to analyze user, app, and device records and determine threats prone to cyber-attacks. 

AI solutions can also be used to augment the operational safety of mobile applications and improve endpoint detection. AI in banking is used in designing apps that can be employed to establish base user behavior and flag unusual user behavior to improve cybersecurity. 

Lastly, AI solutions can also help combat internal banking threats by employing Machine Learning solutions that can analyze user behavior to detect and flag user deviation from their base profile to notify the system administrator. 
 

9. Loans and Credit Card Decision Making

Most banks still use traditional verification methods when approving loan and credit decisions. They are confined to using credit history and scores and customer references to determine the creditworthiness of an individual or company.

Incorporating AI in banking systems helps banks make informed, safer, and cost-effective decisions by investigating the patterns and behaviors of customers with a limited credit history of generating default warnings.

How Can AI Help Banks Combat Frauds?

The banking industry is highly vulnerable to fraud, hacks, and scams as it collects and stores sensitive data. The adoption of AI in banking through AI-based Deep Learning is transforming how banking security works. 

Let us explore the different kinds of fraud challenges that the banking industry faces and how AI helps combat these challenges. 

Challenge:  Phishing 

One of the most common challenges banks face is email phishing. Individuals and sites send fake messages to users via email to retrieve the user’s sensitive information. This information is then used to hack into the system and obtain valuable data and information. 

Solution: 

 AI in banking can be used to develop ML algorithms that can differentiate legitimate emails from spam emails by assessing the details of the sender, subject lines, and content, which helps prevent fraudulent activity. 

Challenge: Identity Theft 

Criminals and hackers can hack into users’ bank accounts and steal their identity by obtaining access to their credentials and changing them so that the users can no longer access their accounts. 

Solution: 

AI solutions can be used to implement stringent security systems that possess features like multi-factor authentication. This ensures that the user gets immediate notification if their password is being changed or compromised. 

Challenge: Credit Card Theft 

Hackers can obtain and access the user’s credit card details through email phishing or identity theft, leading to payment fraud.  

Solution: 

AI-powered solutions are able to learn the user’s spending patterns to obtain actionable insights that make predictions of future expenditures to identify abnormal patterns and help in preventing fraud in real-time. 

Challenge: Forged Documents 

Hackers can access users’ documents and identification to create fake loan and credit card applications, incurring costs and losses to users. 

Solution: 

Through AI-based Machine Learning algorithms, machines can be trained to detect and differentiate between forged and original identification and require user verification before allowing document access.

Moreover, the number of datasets that are being fed into the machine is increasing, meaning the rate of accuracy of detecting fraudulent activities increases as well. 
 

Why AI Is the Future of Banking Automation

There are numerous challenges that the banks face, from operations to customer retention to security, it is quite obvious that banks must invest in AI solutions and adopt an AI-first strategy to improve revenue, mitigate risks, and improve customer relations, amongst many other things. 

An AI-first approach requires banks to shift their focus from being a people-centric business to a customer-centric business that meets customer demands and expectations. 

To meet these customer expectations, banks must overcome internal challenges faced by the existing legacy systems by employing AI-backed solutions. 

To employ and utilize the benefits of AI on a large scale, banks must implement a concrete AI strategy that is developed around the goals and values of the organization. Further, banks need to evaluate the most relevant AI strategy to build trainable AI models. 

The potential of AI in the future of banking is vast. Companies, both involved and not involved in financial services must adopt an AI-first approach.