How is technology shaping the future of the BFSI sector?

Praveen Paulose, MD & CEO of Celusion Technologies.

The pace of technological innovations has been lightning fast since digitization has penetrated the business world. As per reports, 94 per cent of the IT employees believe their organizations have accelerated their innovation pace in the past three years as a result of emerging technologies. Due to technological advancements, the BFSI or the Banking, Financial Services and Insurance are not limited to traditional methods but are coming up with a host of new innovative ways, like automated customer acquisition, virtual assistants like chatbots, automated credit assessments, and engaging customers by extending financial or banking services.

Technological innovations are equipping the world of finance with intensely customized and on-demand experiences, like personalized investment plans or even, requiring companies to reinvent themselves to take these advantages. As new technologies advance, they are empowering the financial or the banking sector with extraordinary new capabilities.

So, let us understand how technology is shaping the future of the BFSI (Banking, Financial Services and Insurance sector?)

Customer acquisition automation
Automation and Artificial Intelligence (AI), already an essential part of consumer banking, will affect operations more deeply intensely in the future, assisting not only in reducing costs but also enhancing customer service. Automated customer acquisition is a smooth, real-time, protected, end-to-end encrypted audiovisual interaction that expedites the customer acquisition process and offers flexibility and freedom to the client. With technological advancement, automated customer acquisition is an investment for the future of the financial sector, rather than a cost centre.

Customer onboarding – omnichannel experience
Technological advancement has enabled banks to follow an omnichannel approach towards customer relations. Services like opening an account can be done today on multiple platforms like Native Android, iOS, cross-platform Flutter based and web applications, which are rapidly becoming the preferred method of delivering services.

Coupled with AI-enabled methodologies, these applications provide a seamless and enriching user experience. Also, the sales, business and operations due to diligence teams also come onto the same platform providing an omnichannel experience not just to the customer, but also to internal stakeholders.

Anomaly detection:
To identify fraudulent activities, banks and financial services firms have implemented a security strategy using streaming analytics and real-time anomaly detection. In order to detect anomalous user behaviour, banks and financial services firms use Security information and event management (SIEM), Data loss prevention (DLP), Identity and access management (IAM), and Threat intelligence and management.

In order to detect unknown abnormal behaviour patterns, such as rare transaction sequences, financial institutions are upgrading their behavioural anomaly detection technology. Machine learning technology enables enterprises to comprehend behavioural patterns by analysing the algorithmic patterns from a vast set of clustered data. By doing so, banks can distinguish between normal and anomalous behaviour.

Risk assessment
Determining the creditworthiness of a customer is quite challenging for banking and financial firms since lending money to an insolvent customer can lead to a disaster. Hence, with the help of data-driven AI, financial enterprises evaluate customer credit history, by scanning accurately through a vast set of data records to recommend customized loan and credit offerings and avoid any default. Banking firms use their mobile app to track transactions and analyze customer data. As a result, the banking firm predicts the risks in issuing loans, such as customer bankruptcy or the threat of fraud.

Debt collection:

Traditional banking strategies risk models are limited to finite data with ancient formulas that are unable to adapt to changing economic conditions. The inability to predict if the debt will go for collections can lead the lenders to incur a loss. When a customer falls behind in their regular repayment, the traditional approach of one product for all can lead to the customer becoming a defaulter. However, AI and machine learning are enabling banking firms to modernize their loan collection. These technologies evaluate vast data from different sources, producing reliable observations about past unidentified client data that leads to delinquency risk.

AI combined with machine learning can swiftly consolidate new data, updating analysis in real-time in ways beyond traditional banking models. For example, with an early warning system and learning to categorize customers, technology enables the banking sector to understand, classify and engage with borrowers and reduce the risk by preventing insolvency and efficiently addressing past-due accounts.

Fraud check while onboarding customers:
Customer onboarding is the initial point of contact where the customer experience is based on the first impression, in return, the bank uses this opportunity to engage the new customer. Due to the traditional banking practices like physical interaction, manual compliance checks, in-person identification checks, verifying original documents, customer onboarding results in poor customer experience and potential customer dropout.

Customer onboarding comprises three essential aspects that are time, cost and compliance. To minimise the time taken, banks are opting for face matching, NLP and OCR for text extraction, as the banks can auto-fill customer information by using KYC API integration to obtain details from KYC registries or collate the details from an organization’s central Customer Information System (CIS). Innovative solutions like VCIP or Video-based Customer Identification Process could be utilised across geography enabling the banking firms to protect their interests by preventing money laundering and financial crimes.

With the adoption of technology in the banking and the financial sector, enterprises will be able to redesign their business models to cater to customer demands with customized, distinctive, and advice-focused products and making banking and financial processes easier for the customers.