In the first part of our blog series we have centered our focus on various operational challenges banking enterprises have been dealing with, how deploying virtual assistants will augment banking CX…
In the first part of our blog series we have centered our focus on various operational challenges banking enterprises have been dealing with, how deploying virtual assistants will augment banking CX, finally the potential Enterprise Bot’s BankAI holds in revamping contact center operations.
Let’s now move to the various applications of intelligent process automation in banking in the 2nd part of our blog:
Generating suspicious activity reports ( SAR) is a regular requirement in banking institutions. However, reading manually through reports and updating details in the SAR form is time-consuming and involves high cost.
Enterprise Bot’s BankAI powered by NLP & RPA not only scans through high volumes of lengthy compliance documents in minutes but also extracts the required information for filing SAR.
Most banks still use rule-based detection systems that often raise false positive alerts. It not only wastes resource hours but also causes user dissatisfaction. At the same time, archaic systems & applications are not capable of identifying today’s sophisticated attacks. AI & NLP-powered virtual assistants conduct smart behavioral analysis to cut down false positives by 20% without compromising on compliance standards.
Manual errors cause banks to lose millions of dollars every year. An AI & ML-based application like BankAI automates the process end-to-end and helps you achieve a high degree of accuracy by capturing, assessing, and sorting huge customer data elements in no time.
Excessive turnaround time is a major problem in customer onboarding. In addition, manual report analysis, paper-based inefficient processes, duplicated data entry, low transparency in the application process, limited communication and security and compliance make the process even slower.
RPA & AI-powered virtual assistants come into the picture here as it completes the KYC authentication and credit checks in a few simple steps. As a result, banks don’t need to ask for customer details; again and again, bots can fetch it directly from the centralized repository whenever required. This makes the onboarding cycle secure, seamless and shorter.
Bots are given access to look into user transactions. AI-powered chatbots use NLP to decode the request sent by the user, be it related to balance inquiries, monthly spending, general account information, address change requests, FAQs, or something else. The bot searches and processes the requests instantly.
One of the major tasks banks need to perform is creating the right profiling of customers dependent on their risk score.
AI-powered bots segregate customer profiles based on their past records. It classifies them based on their risk profile, from low to high. This helps agents decide which items should be offered to a group of customers with higher risk scores.
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