Decision Automation: How to achieve continuous improvement?

We recently hosted a virtual roundtable in partnership with FICO on ‘Decision Automation – How to achieve continuous improvement?’. The conversation considered a variety of aspects around how are you using decision management technology and decision automation today, how to innovate with market uncertainties and much more.

The conversation was chaired by Richard Lagerweij (Business Development Lead at FICO), Vere Millican (Group Executive, Credit, Direct Marketing and Data - African Bank) and Dawid van Zyl (Program Executive Credit Decisioning at African Bank).

In an ever-evolving digital landscape, the need to create efficiencies and sustain consistency is paramount. Organisations are facing increased scrutiny to get the best out of their people and to optimise the time they spend working and making better decisions. How can you achieve continuous improvement in this space?

The key takeaways from the conversation appear below.


Technology and decision automation across the board

One of the biggest challenges organisations are facing is combining the different components in the lifecycle of various technological solutions and then automating processes from here. Organisations need to get to a point where they are using as little technology as possible, sharing as little information across platforms and cross-referencing less, to ensure all the data is in the same place to allow them to scale effectively. A modularised solution can really help with this. Taking a modularised framework and applying it to the right tech stack, which could be implemented largely across the org at a lower cost.


A strong and standardised credit risk infrastructure enables analytic innovation with consistent and improved customer interactions

During the round table, Vere raised the benefits of the FICO Blaze Advisor engine and how that strong platform enables them to improve their business using optimization (an innovative analytics flavour) and how they use Blaze beyond credit decisioning and serve the CRM and customer contact domain.


The accuracy of your data cannot be ignored

When working with technology and decision automation you need to ensure that the data is properly managed, in a full life cycle with complete lineage, as well as being accurate and ‘clean’. This is integral as having the ability to trace decisions to the lowest level quickly, across multiple large batches, allows you to test old strategies as well as new ones to figure out what would be most successful based on the results.

You cannot emphasise the importance of the correct and clearly tracked data. If that investment in the data isn’t done initially then you can apply any technology, but you will not have the success you want or expect.


Leadership buy-in is integral 

When looking at making technological improvements to your organisation, you need to make data at the forefront of the change, and then create awareness, starting with the leaders to drive buy-in and accountability. Attendees agreed that there is a big part here about change management and re-affirming the importance of data and accuracy, which can work really well from a top-down approach. Although a lot of this revolves around the technology, getting the process and way of thinking set up first, with the buy-in and accountability, then organisations can progress forward from there.

There is an enormous emphasis on leadership motivating people to embrace the change in behaviour and process to ensure data is accurate and recognised as an asset.


It's not always about the technology

Attendees agreed that there was a lot of discussion around the more advanced technological changes like (AI/Machine Learning etc), but that is only a percentage of what organisations are doing in the org. Taking this into account they explored how BAU processes need automation too, and that is the majority of what most organisations do. There was an interesting conversation around how organisations, as a whole, are always on a different level when it comes to being ready for the advanced technology, and it varies based on many factors, including the department and leaders, which can have a massive effect on getting the ‘smart tech’ involved in every day.

Sometimes you need to get the processes and the way of thinking in place before you progress into integrating the technology you need to progress.


Taking the above into account, decision automation can be a long journey for organisations to undertake, particularly when there is a skills gap. Sometimes this will require organisations to hire for potential and train people to fit the gaps, rather than

It can be a long journey, particularly if you have scarce skillsets so sometimes you need to find the potential talent and train them. It can take a long time from conception to reality. The business needs to own the change and have data science support it and that’s when you can get true buy-in and long-term success.