CBA Endorses No-Code Platform to Broaden AI Use Finance – Strategy – Cloud – Software

CBA is seeing its investment in no-code machine learning platform maker as a turning point that will enable anyone in the group to build “incredibly predictive models at the touch of a button.”

In this week’s episode iTnews PodcastCBA’s Chief Decision Scientist, Dan Germain, discusses the bank’s AI “democratization” efforts and mindset, as well as some of the major customer-facing victories that their niche field — decision science — has had to date.

Democratization is one of the key drivers behind’s support for the CBA.

The bank is placing a substantial, though measured, bet on’s technology: becoming its “exclusive financial services partner in Australia and New Zealand”, and making the vendor’s ‘AI cloud’ available across the organization’s tooling comes under a broader initiative that envisions CBA as a “centralized AI capability that is for all”.

“We are an AI-first company these days, and in order to do that, we need to enable many of our employees to have access to great intelligence and insight to be able to better serve customers.” Germain said.

Germain compares to “AI for AI”.

“If you think about how data science is traditionally done in most places, you take a problem area, you get the data around that problem area, and you take a data scientist, and they use predictive models. Let’s try to do it,” he said.

“The data scientist cleans the data, thinks of a variety of techniques, codes the model itself, obtains the results, and then trains the model [further], Then they tune the hyperparameters [variables used in algorithmic training]And they can go back and try another technique.

“The process involves a real expert, but continuous refinement can take weeks, even months.

“What does is automate that process, so it will look at every possible combination of the latest and greatest techniques in modeling and data science, automatically sorting through all of them, with features on-the-fly. Will tune, and combine many different models to get the correct result.

“And what you find there is that you can have an incredibly predictive model, literally at the touch of a button – you provide the data, you flag what outcome you’re trying to predict.” want, and then technology will automatically churn out an incredibly predictive data model so that we can use it to be relevant to our customers.”

With an organization the size of a CBA, the ability to automatically generate a model that best suits the analytics task at hand is a powerful concept in itself.

It also gives CBAs the opportunity to rapidly scale up and increase the potential benefits of data science, as there is little barrier to entry, and not every use case requires a data science specialist.

“ was such an attractive partner for us because it democratizes and lowers the barrier of entry for analysts or modellers who do not have a data science background, looking to generate more meaningful insights for themselves and their business partners. For that might otherwise be the case,” Germain said.

“In terms of the size of the CBA organization, you can see how it allows people to generate better business and customer results by empowering them with access to tooling that would have previously been beyond their skill or ability.

“Every Time We Use Our Best People” [on] To deliver the best results in our most difficult and challenging areas, we get good results. We are now able to do this on a scale that exceeds what we have been able to do in the past.

“It opens up new opportunities for things we’ve never done before, and it’s a really exciting thing for our customers.”

While this may lead to a proliferation of models, one of the attractions of the platform is that it can be configured with railings that “contain and control the application of this technology”, and provide complete end-to-end End Process How a model was created can be fully audited.

“It comes with enterprise standard controls around interpretability, so how can we understand how the models are generating results?; to detect bias, so how can we make sure we serve different subsets of customers? Don’t have a disparity in the way we serve?; and really strong controls for anything that gets put into production so we know what, when and under what conditions went live,” he said.

“We can audit it, and that way, we can make sure we have a really strong framework for how we serve customers, making sure it builds a brighter future for all.” in line with our objective.”

Germain says this is not materially different from the scrutiny that already applies to bank modeling.

“Any models that we use within the business are now subject to our Group Model Policy,” Germain said.

“The bank has been running on the model for decades – all banks have – and so we already have a very strong framework that manages the documentation of the model: where do they sit? Where do they live? What data do they have? use the facilities? How often are they audited and checked, etc.

“There’s already an incredibly strong framework in place. Our AI models are no different: they go through the exact same robust regime, and we also investigate some interesting new things that happen around interpretation and disparity with AI. Huh. “

Adding should simplify this process, however, by automatically documenting compliance with the model, making it easier to audit the model from the start.

top talent access

The partnership with also buys CBA access to elite data science talent in the form of Kaggle Grandmasters: the top-ranked people on the Google-owned online community and competition platform for data scientists. makes a point In its marketing that “the world’s top 20 Kaggle Grandmasters (a community of the best machine learning practitioners and data scientists in the world) are the creators” of the company.

Access to those was a drawcard for the CBA; Bank said at the end of last year It wanted these “machine learning engineers and product experts to work full-time on developing new AI solutions”.

“Suddenly, we have access to literally the best data scientists in the entire world working on problems for CBAs and our clients,” Germain said,

“It’s an incredibly exciting thing for us to be a part of.”

Access to’s resources will enhance CBA’s own critical data and analytics teams and capabilities.

Currently, there are “a few thousand” people in the CBA’s data and analytics function, including “a few hundred” people within its subset domain of decision science germane.

The decision science team includes data scientists, AI professionals, experimental scientists and behavioral scientists, among other roles.

“The idea behind decision science was to bring together a mix of people with extensive experience and ideas … to achieve better results than would otherwise be the case,” Germain said.

Decision Science has already produced several major customer-facing services, including: Customer Engagement Engine (CEE) which predicts the next best interaction with the customer for the bankers, and profit finderwhich instructs banking customers to claim a refund or discount to which they are entitled but may otherwise be unaware.

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