AI software can be used for a wide variety of processes and tasks in the financial sector ( full study available to Gartner clients ). Examples include the following areas:
How AI helps you manage expenses: AI solutions can read receipts and categorize them based on a list of accepted expense types or vendors stored in the system. This means your staff only needs to review the expenses that the system rejects or does not recognize.
How AI helps you manage accounts payable: AI can extract and compile data from PDF invoices so your teams can focus on more complex tasks.
How AI helps you with policy compliance: Using natural language processing capabilities and machine learning, AI-powered tools can scan documents for terms related to compliance with policies such as GDPR .
By automating processes with AI, you are not replacing your employees, but giving them time to focus on more complex tasks.
According to Gartner, most finance teams spend nearly half their time gathering and reviewing uk telegram data information to create reports and forecasts ( full research available to Gartner clients ). AI can save teams time, create reliable forecasts, and reduce the likelihood of errors.
For example, you can use AI and ML to predict customer payment habits. If a customer's past behavior indicates that they will pay late, you can remind them to pay sooner than other customers who pay on time. Such a process is called an ML-Improved A/R Process.
Artificial Intelligence for Banking Examples
Storage and information management provider Iron Mountain was able to reduce the time to pay invoices by 40% using this method ( full study available to customers ).
AI and predictive analytics can also help financial firms assess and manage risk: 42% of banks and investment services firms either already use AI for risk management or plan to do so ( full study available to clients ). AI can also help you reduce the risks associated with lending and improve fraud detection.