The powerful money-saving magic of AI in banking
May 08, 2019, by Jessica Munday
The implementation of AI in banking is no secret. Every day new innovations make their way into the customer-facing banking experience, such as chat assistants or AI-enabled budgeting and saving programs and apps.
Due to this technology, the customer experience has drastically improved, which returns high customer engagement and loyalty rates. It allows customers better access to their money and gives them more insight into their financial situation and how to reach their financial goals.
While customer-facing AI is making waves, the real money-saving opportunity for AI in banking is applying this technology to backend processes to accelerate organizational productivity and increase capital efficiency.
By 2030, AI is expected to save financial services more than one trillion dollars across banking, investment management and insurance. Banking alone will save an average of 25 percent, toppling $450 billion in savings by optimizing wasteful bank processes.
Many banks are stuck in traditional, manual processes. Employees spend hours on laborious computer tasks, much of which is repetitive. This is where automation can play a major role.
Let’s visit a common example: a loan application.
A potential customer applies for a car loan. A bank associate enters the data into a bank database and then assesses that person’s risk for taking out a loan. Once approved, the customer sends over the vehicle details, which are inputted to the database. The money is processed and wired to the car dealership. The customer then provides proof of insurance and registration, which are updated and entered into the database as well. Much of this is done via the “copy-paste” method or manually by the banker.
This is just one example of an inefficient process. Most banks offer several different loans and handle these processes across various departments.
AI alleviates and automates a number of repetitive bank processes to increase the productivity of each employee. Here are some example use cases for AI in banking:
Automating repetitive processes
Technology that can automate repetitive tasks is a major value prop for AI in banking. It reduces the time it takes to complete a task or eliminates a process altogether, allowing bank employees to focus on productive work.
Automate common customer requests
Going back to the loan example, we see there are several times when customers information must be updated by bankers. Using natural language processors (NLPs) and intent detection, an intelligent automation platform can automatically perform these updates.
Eliminate “copy-paste” tasks
AI enabled with field mapping technologies eliminate the need for bankers to copy and paste information from one location to another. Let’s say an underwriter gets an application and all of that information needs to be copied into the underwriting system; AI can scan and map the proper information to the corresponding fields in the database.
Using NLPs, an automated system understands a request to schedule a meeting and performs the appropriate next steps. This could be drafting an email message with availability or placing a hold on the banker’s calendar.
Reducing time spent on analytical processes
Augmented tools can influence decision making to help people make smarter choices. Humans can then solve complex problems in a shorter amount of time and complete the overall process more quickly.
Underwriters perform massive amounts of manual data entry and must weigh various factors to assess the risk of a potential customer. AI reduces the mundane tasks associated with underwriting and augments the underwriter’s decision making by assessing the customer’s information as well.
Process claims & disputes
Claim and dispute processing are also paperwork-heavy processes. AI automatically ports over customer data from various systems and can assess the likelihood that a fraudulent/incorrect purchase was made. This gets more claims and disputes done faster.
AI is expected to cut the cost of underwriting and collection systems in half by 2030, resulting in a total cost saving of $31 billion.
Finding new revenue opportunities
It costs a bank $7,700 to acquire a new customer. AI helps bankers find new revenue streams from existing customers by offering up cross-selling recommendations and providing them with best next steps to close a new account or loan.
Give cross/up-sell predictions
Only 20 percent of banking customers have a mortgage with their primary bank, which means most banks are missing a major opportunity. AI can suggest that a banker cross-sell a mortgage to an existing customer who is showing home-buying behavior.
Provide best next step recommendations
AI can provide bankers with the next best steps to improve conversion rates. Recommendations are based on data from previous steps that led to successful conversions.
The results for the above use cases for AI in banking are the following:
- Improved access to customer service
- Shorter application processes
- Higher quality banking data
- More productive employees
- Boost in customer base and revenue
AI will change banking as we know it and many big players have already implemented on the front and back-end. Banks and financial technology companies say deploying AI and machine learning is their top innovation priority.
It’s likely that AI will be a key differentiator when it comes to the future leaders of the banking industry. Seventy-six percent of banking executives believe a competitive advantage will be determined by the tools and partnerships financial organizations chose to integrate, including AI. This competitive advantage will come from using capital more efficiently and being more productive than the competition.