My summary of Finalytics.ai before using it — Finalytics is a startup specialising in analytics for banks and other financial institutions.
How does Finalytics explain itself in the first minute? “Humanize The Digital Experience” reads the strap-line on the Finalytics website. Through Finalytics, financial institutions can use AI and access a “segment of one digital experiences for visitors informed by behavioral, transactional, and 3rd party data.”
Reading about Finalytics’ value proposition reminds me of a piece titled “The New Moats” by Jerry Chen, Partner at Greylock. In the piece, Chen writes about “systems of intelligence” where companies combine multiple data systems and systems of record. He argues that for a startup to thrive around Customer Data Platform (CDP) incumbents like Oracle, IBM and SAP they need to combine CDP data with other data sources (public or private) to create customer value.
Chen suggests three areas where businesses can use AI techniques to build systems of intelligence: customer facing applications, employee facing applications and infrastructure systems. Using Machine Learning in combination with data, a business process, and an enterprise workflow, you can create the context to build a system of intelligence.
My expectation of Finalytics is that it wants to firmly position itself as a provider of a “system of intelligence” for financial institutions (FIs), combining different sets of customer data (e.g. transaction, demographic and behavioural data) to provide FIs with valuable customer and strategic insights.
How does Finalytics work? — The Finalytics website lists several use cases for its product: expected value probability, optimising branches, dynamic website content and “Next best financial product”.
The main premise of Finalytics.ai is to provide actionable insights based on data that’s continuously being analysed by an AI engine. It looks like Finalytics applies ML to predict customer lifetime value, learning from a combination offline (branch) and online data, using this data set to create association, connections and understand similar behaviour.
This ongoing analysis and the resulting insights will help FIs personalise their customer journeys and recommend the “Next Best Financial Product”. Predicting a customer’s next best action is the holy grail for all marketers across the globe, and I’m curious how Finalytics will crack this nut: can its platform accurately predict customer needs, and recommend financial products that customers don’t have but might need?
Interestingly, Finalytics have architected their platform in such a way that it can integrate with cloud-based solutions such as Salesforce and Google Analytics. In a recent interview with Craig McLaughlin — CEO of Finalytics — McLaughlin talks about Finalytics’ focus on US-based Credit Unions and Community Banks, helping them to make good use of all the (customer) data that they have at their disposal. McLaughlin sees the challenge for his (target) customers as one where they want to use digital both to maintain what they’ve got currently and to grow to a next generation of customers.
Main learning point: Finalytics isn’t the first and certainly won’t be the last of companies to try and tackle the problem of banks’ large but fragmented data sets. Can startups such as Finalytics use AI effectively to generate predictive, actionable insights for their customers? I firmly believe in the potential of AI to both analyse and predict customers behaviour, and I’m keen to see real-life examples of the tangible use cases and value for (large and small) banks.
