AI solutions are nothing without quality data

AI solutions are nothing without quality data

Lydia Coyle
by Lydia Coyle

Artificial Intelligence solutions are nothing without data, and banks certainly have a lot of it. The challenge is around quality rather than quantity, and an inability to identify and really harness the types of data needed to design, build and develop an effective AI solution.


There are several capabilities financial institutions can build that will serve the organisation well when executing AI projects, and a number of steps that can be taken within the project itself.


Firstly, banks need a centralised data hub. This should include a data model that rationalises data points across the organisation – transactional, personal, risk, financial, digital usage, product and external data sets. The hub can be built upon a cloud-based architecture that allows data to be identified, accessed and manipulated as a particular business area sees fit. We fully appreciate that this is no small task so it should be an ongoing exercise that lays the foundation not just for AI projects, but for nearly all change initiatives across the bank.


A hub for production data will allow the business to analyse how an AI solution can be applied and will provide historic data that will be used to train AI models. But crucially it is unlikely to be suitable for the early phases of an AI project due to data controls that need to be adhered to in development environments.


During development, the organisation will want high quality, robust test data that can be used without restrictions in sandbox and test environments. The capability to produce and harvest high quality test data can be purchased through a supplier or developed as a function to speed up the rate at which the business can quickly test their hypotheses in a low risk manner.


With the ability to easily source the right quality and quantity of both test and production data, each individual project then needs to consider how it will prepare and use that data for its specific use case.


Up front analysis of the problem at hand, the end goal and the available data is essential to developing and implementing an effective AI solution. Ultimately the data determines what the algorithm does.

To do this, project teams should analyse, prepare and constantly monitor the data for their AI solution.


Read our whitepaper “Navigating AI Financial Services” for more information and for the Woodhurst Blueprint© for building Artificial Intelligence capabilities

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