Data-driven personalisation: Tailoringfinancial services for the digital age

As industries across geographies get hyper-competitive, personalisation of products and services has emerged as a key differentiator in almost every vertical, including financial services. In the digital era, it is understandable that data-driven personalisation is emerging as the need of the hour for financial services. Consequently, customers expect personalised services from banks, fintech firms and other financial entities. Furthermore, it’s taken for granted that banks and fintechs will recommend and provide value-added services proactively, even if the customers haven’t asked for or are aware of such options. 

Personalised Cross-selling and Customer Satisfaction

Consider car loans. A senior citizen in the early 60s may have been granted a car loan by a bank or fintech firm. However, the loan recipient’s family has an unstated concern about the loan repayment if an unfortunate event befalls the applicant. While lenders have no worries as the car is hypothecated with them, nonetheless, they sense the family’s concern. Therefore, the car loan is insured for a modest premium during its entire tenure. The loan recipient and his/her family are now at ease that there will be no issues regarding the repayment. The personalised cross-selling of an insurance policy along with the car loan then generates great goodwill with the recipient and the family, ensuring long-term loyalty and repeat business from them. 

In this case, readers will note that human intuition and intelligence led to the cross-sell. But in most cases, artificial intelligence and the use of big data analytics highlight personalised offers of financial products/services to address the distinct requirements and preferences of customers. 

There’s a good reason for the expanding growth of personalised services. A survey by Accenture shows that 75% of consumers are inclined to avail of services from a bank providing personalised services. To achieve this objective, fintechs and other BFSI players use a data-driven approach that garners information from customers’ browsing behaviour and transaction history. In turn, this gives insights into their financial needs and habits. 

By decoding the data, fintechs can provide personalised recommendations, bespoke offerings and targeted marketing messages. Such personalised offers help fintechs stand apart from other financial players while promoting higher customer satisfaction and retention levels. 

Apart from data gathered directly from customers, financial entities can access data from partners and third-party sources to enhance consumer experiences. Most consumers believe an enjoyable experience is as important as the products or services offered by lenders. This highlights the criticality of using an innovative business model that serves customers according to their needs and preferences. 

The Advantages of Data-driven Insights

Data-driven financial services can transform the industry landscape in multiple ways, such as increased efficiency and the ability to predict consumer behaviour. Indeed, predictive analytics is being used by most financial services firms as it allows them to forecast the future behaviour of customers and their financial needs. This permits lenders to proactively provide personalised offerings to customers.

Additionally, through data insights, banks and fintechs can streamline and enhance internal processes with AI, ML (machine learning) and robotics. This can curb operational costs and risks while boosting their overall performance and operating margins via enhanced efficiencies. 

Nevertheless, it must be emphasised that AI and ML algorithms are meant to improve the efficiency of human resources and not replace them per se in every way. AI and ML can make better value judgements vis-à-vis the inherent risks of lending to some customer cohorts. For example, AI/ML algorithms are crucial in predicting the possibility of delinquency and financial crimes. Data-driven predictions help pinpoint the likelihood of fraud, improve credit decisions, boost collection strategies, anticipate demand for liquidity and mitigate overall risks while simultaneously controlling costs. 

Conversely, robotic automation can free humans from mundane tasks, giving them extra time to manage customer interface functions that advance engagement, satisfaction and loyalty levels. Here, AI-enabled chatbots could be most useful in ensuring 24/7, real-time financial help to customers by addressing their queries and concerns. Customer insights from AI could then help the sales team maximise sales by increasing cross-selling/upselling efficiencies. Lenders can also leverage consumer behaviour data to build predictive models facilitating more relevant cross-selling proposals. 

Addressing Safety and Security Concerns

It must be mentioned, though, that the rising use of digital tools has triggered a parallel increase in phishing and other cybercrimes. As a result, lenders must pay equal attention to ensuring the highest levels of data security and privacy for customers. Companies addressing the data safety and privacy concerns of clients stand a much better chance of building a base of loyal customers. 

Keeping customer concerns in mind, fintechs use digital tools such as biometric authentication and blockchain to safeguard consumer data. By checking distinct physical traits such as fingerprint scans and facial recognition, biometric authentication ensures extremely convenient and secure access to financial services.

Coming to blockchain, it creates a secure, tamper-proof environment to store data. Besides, this digital ledger technology ascertains entries are done transparently and every entry can be authenticated to verify its source and genuineness. 

Thanks to these benefits, digital technologies and data-driven personalisation of financial products are leading to greater pan-India penetration of financial services, driving inclusive development. Given the immense benefits of AI-enabled, data-driven personalisation, financial firms that leverage these technologies will enjoy a clear competitive advantage over their peers. 

The article has been written by Ram Kumar, SVP – Data & Analytics, mPokket