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How Banks Can Unlock Data Monetisation to Stay Ahead of the Curve.

With high internet penetration and advancements in mobile technology, people generated a whopping 2.5 quintillion bytes of data every day in 2021. This is only expected to grow, reaching a jaw-dropping 463 exabytes per day by 2025. This means companies, especially banks and financial institutions, have a treasure trove of data at their fingertips. By harnessing the power of this data, financial institutions can generate insights into customer behaviour and preferences, and tailor their products and services accordingly.

Sources of Data for Financial Institutions Banks have greater access to customer data than other companies. This can be combined with external data to fill in the missing pieces.

Consumer Data

  1. Sociodemographic profile

  2. Online interactions

  3. Payment behaviour

  4. Purchasing preferences

  5. ATM usage

  6. Frequency and types of investments

  7. Social networking platforms

With these, a financial institution can estimate the wealth status, lifestyle, risk appetite and spending habits of customers.

  1. Corporate Data

  2. Company performance

  3. Company risk profile

  4. Vendor, employee and customer payments

  5. Industry dynamics

  6. Market dynamics

With these, a financial institution can estimate the health and growth prospects of companies.

How Banks Can Monetise this Data

By harnessing the power of AI and ML, massive amounts of data can be filtered, organised, and analysed to pair innovative offerings with personalised customer services, giving banks a way to stand out in a largely undifferentiated market.

Sound data analysis enables banks to identify and estimate a customer’s varying needs, and market specific products in a personalised way. Banks can create different product portfolios for different customer segments and determine which products are missing for each customer and actively cross-sell these. Data-led personalisation allows banks to customise the way they greet and treat their customers, while offering the most appropriate bouquet of products, at bespoke pricing. The data can also be extrapolated to the customer’s peers (people with similar demographics).

These strategies increase customer engagement and boost revenues. More importantly, they help financial institutions form stronger relationships, increasing customer loyalty and reducing churn.

Data can also be used for fraud detection, improving credit decisions, making collections strategies more effective and forecasting liquidity needs. AI and ML have also proved useful in identifying cost pools, allowing banks to streamline processes to make them more effective, yet more cost-efficient. Financial institutions have also used data analysis to automate different processes to curtail process costs and operational risks.

Effective data monetisation is possible only if there is a focus shift towards it. Data needs to be treated as a highly valuable asset that must be honed with the use of powerful AI/ML-driven strategies.

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