Banks occupy a privileged and trusted position. Social media companies may have insight into what is attracting our attention, but banks can see what people are willing to pay for. Accessing this source of Big Data is simply the latest way for banks to deliver on their core promise: to listen to customers, create a service that benefits them and offer that service in a personalised way.
While banking data must be treated sensitively and securely, financial institutions have started to look beyond risk and focus on how data can deliver benefit to customers: witness how data-led organisations use insight to increase customer satisfaction and revenues while reducing costs and mitigating risk.
Using data to provide banking services is not a new concept. But banks are becoming alert to their unique data and the value this can drive for customers.
A recent Accenture survey of 1,440 C-level executives shows that companies with higher growth rates give importance to technology and to organizational characteristics that depend on data for their development. These companies focus on mastering leading-edge technologies and data manipulation to drive business innovation at unprecedented scale.
Building the data foundations
So, where to start? Traditionally a technology domain, data is central to value generation in the world’s most innovative banks. Increasingly the Chief Data Officer reports to the CEO, charged with promoting strategic use of data to power growth.
For CDOs, creating the foundations that a modern data-led bank needs to maximise value has four parts:
- Right operating model: Organising multi-skilled data and analytics teams operating in partnership with the business. These teams should hunt for insight through experimentation, demonstrate how that insight improves business outcomes and implement solutions driven by that insight.
- Flexible technology: Choosing technology that allows rapid development of data pipelines, platforms for data analysis including machine learning and AI, and automatic deployment and monitoring of end-to-end data-driven solutions.
- Strong governance: Traditionally, data governance focused on protection, provenance, timely delivery and quality. It’s now increasingly concerned with acquisition and exploitation of more data (both internal and external). More data creates more opportunity, but while 50 percent of people are willing to provide their data in return for rewards, banks should ensure this exchange is conducted ethically, with responsible use of AI.
- Scale: Data-led solutions are not isolated. To move into production, they must include business change, integration with the organisation and ongoing monitoring, retraining and support. No more Death by Proof of Concept.
Becoming a data-led organisation
With the foundation in place, it becomes simpler for banks to use data to drive value – in one of five ways:
- Data-led personalisation: Tailoring products and services. Whether this involves bespoke pricing, insights to promote financial well-being, or matching specific life needs with specific services, using data to personalise banking improves customer engagement and increases revenue. A major global bank used personalised insights delivered to customers to increase savings balances by £60m in just 18 months.
- Data-led prediction: Richer data provides opportunities to predict the future. Enhanced prediction improves credit decisions, fraud detection, collections strategies and forecasting of liquidity needs—decreasing cost and mitigating risk. For example, leaders have made significant strides using AI to predict financial crime.
- Intelligent insights: Unlocking data from IT and delivering to the business. This may include self-serve analytics through Google-like interfaces, using analytics to identify cost pools or allowing front-line staff to mine industry and customer trends. Such intelligent insights from data boost revenue by enabling the business to make accurate and nimble decisions, while reducing expenditure on BI/MI.
- Intelligent automation: Banks can use data and automation to reduce business process cost and operational risk. There are various flavours—including augmenting robotic automation with AI so machines make value judgments; creating hybrid workforces of humans and machines; and using data gathered through automation to fix upstream issues or remove processes.
- Digital products and services: Banks can use data to create new products and services, and ultimately revenue streams. This includes monetising banking data itself (for instance, by aggregating customer behaviour and providing insight to RMs); or using banking data when interacting with non-banking institutions to develop an ecosystem of services. A US bank, for example, partners with an auto marketplace, allowing customers to buy vehicles direct from its website.
The path to value
Using data to provide banking services is not a new concept. But banks are becoming alert to their unique data and the value this can drive for customers. A bank that has built solid data foundations, and is focused on becoming a data-led organisation, is best placed to be among the industry’s future leaders.
My thanks to Owen Smith-Jaynes and James Forrester for sharing their expertise on this topic.
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