Frode Berg, Managing Director, EMEA, Provenir, discusses how the secret to a successful banking sector strategy lies in data-driven decisions and user experience that keeps pace with other everyday digital platforms.
In today’s on-demand digital world, where the likes of Netflix, Spotify and Amazon have set a high bar for seamless, personalised experiences, consumers around the world expect nothing less from their bank.
To illustrate this point, a recent McKinsey & Company study revealed that banks using customer analytics have seen a 20% increase in satisfaction scores and a 15% revenue boost.
By embracing digital thinking and harnessing the power of data analytics and the smarter Artificial Intelligence (AI) innovations that are entering the market at pace, banks can successfully pivot towards hyper-personalisation, delivering tailor-made services in real time.
This shift not only increases customer engagement; it also serves as a key differentiator in a crowded market. With the rapid adoption of customer analytics by FinTechs and neo-banks, traditional incumbents must develop targeted data-driven strategies to remain competitive and retain customers.
Additionally, integrating advanced data analytics into credit decision-making processes enables much more precise risk assessment, empowering banks to extend their services to a broader customer base while maintaining security and reliability.
This is crucial in light of the current economic climate, which poses significant challenges for both banks and their customers. With people struggling amidst a fierce cost of living crisis, their economic situations are shifting and risk profiles are changing from day to day. This means that banks have to review their existing lending models, which exclude a significant part of the population.
The use of prescriptive analytics that leverages alternative data to provide a more holistic picture of someone’s true financial situation can help banks and other lenders lean into financial inclusion whilst expanding their addressable market. Because AI can identify patterns in a wider variety of data types – such as telco data scoring, employment verification, social media, income verification and more – it can power highly accurate decisions, even for no-file or thin-file consumers.
The tone has been set. Customers are increasingly expecting hyper-personalised experiences from traditional banks. But how can large incumbent banks, who often face challenges with their legacy infrastructure, shift to a more customer-centric approach?
Here are some tips, which hopefully provide a useful start.
Deploy a more agile tech stack
Firstly, banks will need an agile tech stack with seamless integrations so that they can monitor every aspect of their business in real-time.
Secondly, they need innovative products that have a broader reach. For example, Buy Now Pay Later (BNPL) is popular because it effectively reaches an underserved population. With BNPL, hyper-personalisation is about financial services aligning themselves with the best merchants that drive up their customer base and reach.
Modern tech stacks should include access to lifestyle and contextual data, such as social media, to provide banks with a more complete picture of prospects so that offers can be better tailored to their specific needs.
Leverage the latest AI innovations and Machine Learning
When it comes to lending, which is ultimately how most banks make the bulk of their profits, AI and Machine Learning can help them meet their customers where they are digitally present.
Drawing on contextual and lifestyle data enables banks to use new marketing models driven by AI. As an example, Amazon doesn’t know a customer personally, but it does know if a person is searching for a video game console – and so can suggest a video game.
Applying this to a financial services scenario, a consumer may get a mortgage online, and then a few years later, the provider could send them a message asking if they need a loan for home improvements.
Harnessing data quality for hyper-personalisation
Both AI and ML rely on data. For banks, data serves as the lifeblood of their operations, enabling them to understand customer behaviour, assess risks and deliver personalised services. However, ensuring the quality and integration of this data presents a significant challenge. According to Provenir’s 2024 Global Risk Decisioning Survey, which draws on insights from 300 financial services decision-makers, 38% of respondents identified data quality and integration issues as primary obstacles hindering the delivery of personalised offers.
High-quality data enables financial institutions to enhance risk management practices, improve regulatory compliance and drive operational efficiencies. By leveraging advanced analytics and AI-driven technologies, organisations can extract actionable insights from their data, empowering them to make informed decisions in real time. In order to do this, banks must invest in data management, governance, and analytics capabilities to deliver hyper-personalisation to meet the changing needs of their customers.
Whether banks now choose to partner with FinTechs to speed up their journey to hyper-personalisation or compete against them, there is one certainty: they need to act now to meet the raised demands of their customers and prospects.
If they don’t, they will soon be left behind.
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