How RPA, NLP, and AI is changing the landscape in banking compliance

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AI in the Banking sector and the capability map report gives us various AI approaches and operations within specific banking functions. By considering them on their level of funding, evidence of ROI and adoption at large banks, and more. Here we discuss how and where banks are using natural language processing (NLP)– The technical and detailed description of the machine learning model behind an AI product.

Banks and Financial institutions are tightly regulated industries, and they continuously face complex regulations or changes to existing ones. This regulation calls them to strengthen their compliance framework, governance, oversight, procedures, and platforms; this is where the strength of AI lies. Today, a significant part of banking compliance involves human intervention, like whether to approve or reject a new function of a customer during onboarding, know your customer (KYC), whether to close an alert, or to investigate the suspicious behavior of the customer, and so on.

There are also compliance activities where human intelligence is used to complete specific tasks, e.g., verifying documents against data submitted by customers (say KYC documents or loan collaterals), analyzing new regulations (or changes to existing regulations) and deciding on how to implement the same process in banks, and many more. 

From machine learning (ML), both supervised and unsupervised, to natural language processing (NLP), Chabot’s, facial and speech/voice recognition – AI has the entire space covered in terms of facilitating the said tasks with limited human involvement!

Customer onboarding – NLP based extraction of customer KYC documents is now accounted for document verification and in few cases, direct customer data entry by using onboarding documents. ML is replacing manual approval of various KYC workflows, as cognitive RPA is a new age KYC approval mechanism.

AML- Alert case management is also being reimagined by meeting both RPA and AI. RPA is being enabled in many banks to complete the manual steps of information gathering and populating the results in an investigation report. ML used for decision making on reports. And by closing false alerts and finding out ways for the next level of detailed investigation.

Regulatory change management- RPA and AI can automate large parts of this essential and time-involved function. NLP can is used to read the regulation documents, capture the obligations required to be implemented by banks, and the business lines impacted due to such regulations. RPA can then pick these inputs and generate workflows for the respective business lines for implementing the changes. Post-implementation, RPA can again be used to collect inputs required for assessing the maturity of the implemented changes and collate the same in an assessment checklist.

Limit breach and excess management – NLP can get supporting information from emails and customer documents. Supervised ML is used in decision making whether to approve or reject a breach just by generating a report on the breach along with a recommendation. The risk officer can accept or override the same

KYC data remediation – An automated remediation process can be enabled using NLP based text of customer documents available with the bank, as well as extraction of KYC data from customer emails, interactions with Chatbot’s, and more. Chatbots utilized for seeking specific KYC information and documents from customers, based on the workflow for the particular customer.

The regulatory process of Banking is dynamic and more complex. The upcoming digital technologies promise to make regulatory compliance more agile and efficient. This is done with the help of an automated process, employing valid solutions that are adaptable for the business to grow, and at the same time, significantly driving down compliance costs too. Some of these technologies are robotic process automation (RPA) and artificial intelligence (AI), which are transforming the regulatory compliance in banks. A lot has been discussed about RPA’s capabilities of automating manual tasks that are manual, time-consuming, and do not involve decision making.

RPA and AI are reanalyzing banking compliance, as more bank implements varied and innovative use cases, benefits start to accrue.

This journey of innovation accelerates a whole new digital world of compliance, which in turn is expected to develop as the new norm in the coming future.

Robots and AI are not just science fiction anymore – they are slowly finding their feet in the real world too!