Major banks are ahead of the curve when it comes to adopting AI as a business strategy - an important task for any large corporation that wants to edge over its competitors. Banks have started using AI and machine learning opportunities for front-end and back-end operations, which has already led to overall successful business outcomes.
With the intersection between machine learning and finance just beginning, let's examine how banks are using AI today and how adopting AI strategies will impact key aspects of their operations - for the better.
While banks perform a variety of tasks and functions, there are many key areas that prove that AI is a good fit in improving operations and profitability. Here are four major use cases of AI and machine learning in banking operations so far:
1. Customer Service
Customer service is an important aspect of banking and can make a huge difference in the bank a prospective customer chooses. This is an area where banks are increasingly experimenting with AI to improve customer relationships and overall customer-bank interaction.
Conversational AI is already transforming banking customer service into useful chatbots that provide the customer with a more personalized online and mobile banking experience. One of the biggest players in this scene is Erica of Bank of America, the first virtual assistant widely available to use on the Bank Mobile app. Chatbots like Erica can guide customers through standard banking activities such as viewing balance information or transferring money. They can also push new products or services at the right time, leading to more successful customer adoption and greater ROI to the bank.
Virtual assistants use predictive analytics to determine the right way to direct customers and smooth the process of engaging with the bank. Users can interact with these bots by texting or tapping into the commands on their screens. The Virtual Assistant reduces the need to call or bank directly to the bank, saving time for both parties. With 24/7 access to virtual help, banking hours can finally become a thing of the past.
2. Fraud prevention and security
Identity theft, fraud, and security breaches are common in the banking sector due to personal data and money. Data security is critical to a successful bank operation and maintaining customer confidence. Obviously, banks are targeting this area with AI, which can detect frauds more quickly and more accurately, without the risk of human error by ignoring any data or misunderstanding patterns.
AI detects deception by prescribing predefined rules and analyzing a person's past behavior. For example, if someone who has previously made small purchases suddenly becomes too big, the machine will flag it as a fraud and contact the customer immediately. AI is also being used to authenticate and identify customers when they engage with their banks.
With the troves of personal data to protect, banks are also investing in AI as a cybersecurity tool to prevent future cyberattacks.
3. Portfolio management
Investment portfolios are usually held by a financial advisor or customer. AI is moving away from that paradigm by replacing human decisions with more complete and accurate analytics and risk assessment, ultimately helping to increase portfolio value. AI helps expand portfolios by scanning the global market for new investment opportunities, providing real-time data to inform decisions, and delivering market sentiments globally.
Because virtual assistants are able to offer personalized investment advice based on the risk level of personal and current assets, all of this is highly correlated with the level of customer service.
In all cases, human beings make final decisions but have extensive data and recommendations to inform their choices.
4. Credit and lending decisions
Credit and credit decisions have historically been drawn by human analysis of credit scores, credit history, and other past behaviors. This is not an exact science and banks often lose money due to faulty or missing data in the database or due to human error.
AI is the next evolution to answer this problem. We can quickly assess the data from a prospective borrower and determine the potential risk and profitability of lending that person or credit against known behavior, patterns and market trends of others like him or her.
Using machine learning in this way, banks get a complete picture of risk and potential returns for each individual, which leads to safer decisions and fewer people avoiding their debts.
Read more about the applications of AI in banks in this Enterprise Edges article by Ryan North and this Forbes article by Sani Abdul-Jabbar.
Advantages of AI in Banking
AI will forever change the way banks work, inevitably helping to have a more comprehensive, financially beneficial experience for both the bank and the customer. Experts predict that AI will have many important impacts on banking:
1. Reduce operating costs and workload
By integrating AI into operations, banks reduce the need for manual data entry and other human processes, which often lead to errors. This not only saves time for the individual and the bank but also eliminates costly mistakes.
Moving to conversational AI options, such as virtual assistants, will keep workers from answering standard questions and performing basic transactions. Instead, bank workers can focus on high-value tasks such as deepening customer relationships and matching the customer to the right services for their needs.
2. A new era of regulatory control
Banks are already one of the most regulated entities in the world, and they must follow strict government regulations to prevent or prevent financial crimes in their systems.
With AI's ability to detect fraud by integrating with behavioral analysis and cybersecurity systems, banks can capture financial crimes much faster and with greater accuracy than human beings, making them more compliant with regulations. It also reduces bank risk. On auditing customer behavior, AI can log in to key models and other information to report to regulatory systems, meaning less human data entry is required.
As machine learning is used in banking with a higher frequency, these changes are expected to improve financial terms.
3. Improved customer experience
Banks that use AI can serve their customers faster, more efficiently and at all times of the day. The ability to answer questions and execute basic transactions is at the customer's fingertips. Trust between customers and their banks will grow over time, thanks to secure data and improved regulatory compliance.
While AI can provide personalized insights and connect customers to products and services that fit their needs, the relationship between banks and customers can be seen as deepening and evolving when they need it.
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