CASHING IN
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 .
Frode Berg , Managing Director , EMEA , Provenir
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 customercentric 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
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