AI is an evolving technology that promises different industries a gamut of cutting-edge techniques and solutions. In areas as diverse as driverless cars, home energy systems, and investment portfolio management, AI is already being applied. It will also affect accounting and auditing. Artificial intelligence (AI) allows a complete data analysis and identification of outliers or exceptions.
Each bank wants a robust system that can help them understand the concerns in advance and report them on a real-time or regular basis for further investigations and mitigate the risk. Auditors can improve the detection of fraud by creating sophisticated machine-based learning models. Deep learning, an AI form that can analyze unstructured data such as emails, social media posts, and conference call audio files is set to transform the audit further.
Account reconciliation is a major activity that must be carried out by the bank in order to have checks and balances on the system. As part of automating the reconciliation process, AI/ML plays a key role. They help identify the pattern offered in the algorithm to check different scan patterns on different sources of data, and data could be both structured/unstructured data, fix issues, and provide the auditable recon report as expected by the audit team. Machine learning can be used to encode accounting entries automatically. More will be built on data control, rules, and validation checks to avoid the current problems in the future.
Using Robotic Process Automation (RPA) cognitive bots, Bank can automate critical processes and create workflow models. As and when a user attempts to override or ignore exceptions, the system ensures that workflows for the respective processes are triggered as per the preconfigured setup. Banks need critical processes to be identified and RPA implemented.
BOTS are created and taught as part of the migration process or legacy system upgrade to the new system to fulfill the data validation check. Simply based on predefined algorithm bots are able to validate with existing data, arrive at the required data and supplement the same as the upgrade part. This will minimize the error of reporting incorrect data while preparing any MIS or regulatory reporting.
AI uses profound learning to speed up a wealth of internal audit function processes. This applies in particular to contexts that require a resource-intensive effort using digitized documentation, ranging from advanced search methods to segmentation of documents. In the case of an internal audit, applied deep learning paves the way for better, faster, business outcomes.