Exclusive portal designed for our Customers and Partners

Log in

Request for Access

countries flags

How Explainable Machine Learning Can Help You Get Actionable Predictions for Your Business

Feb. 19 2020 by Francesco Morini, Director - Global Services - Analytics and Innovation - CCH Tagetik

Performance Management Business Intelligence & Analytics

There are more than 582 million unique people in this world in the process of starting or running their own business. This also means there are 582 million unique ways of doing business. Indeed, while some business models might borrow elements from one another, no two business models are exactly alike.

To survive in a crowded industry of any kind, companies must hone in on unique aspects of their business model. A great way to do that? Select business partners that can help you understand the value of different — maybe even out-of-the-box — growth opportunities. As a business partner to many growth-minded companies, we at CCH Tagetik believe the key to growth lies within the fast-advancing technological landscape. That’s why we’ve turned our attention to the predictive precision of machine learning (ML).

How Machine Learning Can Help Identify Growth Opportunities

We believe that ML has the potential to identify the most significant and fruitful growth opportunities specific to your business. As we all know, any good strategy is an evolution of transforming insights into decisions and decisions into actions to deliver the real value: foresight.

ML has the potential to give companies the power to extract precise forward-looking intelligence from their data efficiently. Not only can ML result in greater process automation, productivity, and compliance, but — if used correctly — ML has the power to elevate the potential of data usage. Using ML, companies will be able to create predictive forecasts and predictive sales figures that promise to optimize costs, plan HR, pricing, evaluate risks, segment customers, and more, well in advance of traditional forecasting methods.

While ML sounds so powerful, it’s natural to wonder: what’s the holdup? Why isn’t every organization taking advantage of a technology that has so much promise?

Despite the excitement surrounding ML, finance experts are still struggling to deliver on their artificial intelligence (AI) vision. This is due to a combination of 1. data scarcity when it comes to preparing data for ML engines and 2. limited domain knowledge surrounding how to use AI. This struggle became evident at the last AI for Finance show in New York.

The Biggest — Perceived — Challenges of Machine Learning Adoption

AI for Finance's audience of some 150 finance experts provided pointed input on the topic of AI, especially around the need for ML solutions in the finance space. Here’s how they ranked the ML and AI challenges they faced.

It was no shock that the vast majority of attendees were frustrated with the amount of effort needed to pre-pare data for ML and data science. The process of making data available for ML is very involved. There’s a gap between real data and actionable data, and every ML approach requires significant data aggregation to pro-duce a dataset that can actually be used.

What was surprising was that the time needed to build ML models outranked the cost required to access do-main expertise. Additionally, it was surprising that the need for model transparency that was a concern for many — but not the lack of data scientists.

Why is this so? Well, ML models can lack transparency. Many of them create high power prediction variables that have no practical business value and can’t trigger any action. Furthermore, these variables are often not interpretable, which is a big concern for regulated markets. When the machine makes a prediction, you must be able to track why it made the prediction it did. The ability to explain and trust the outcome of an ML-driven business decision is a critical aspect of finance’s data journey. We call this “explainable ML” — a critical ability when it comes to ML in a finance software context.

It’s a non-negotiable. Finance *must* be able to trust the predictions output by their ML technology. The transparency, accountability, and trustworthiness decision support systems based on AI and ML must be fool-proof. This is especially true for companies in industries subject to serious regulatory mandates, like in banking, insurance, and healthcare.

Tap into the Power of Explainable ML with CCH Tagetik

CCH Tagetik is committed to driving innovation and creating solutions that make explainable ML part of your company’s AI success story. We don’t do technology for the sake of technology. We do technology to deliver actionable value to our customers.

For these reasons and more, we created the CCH Tagetik Finance Transformation Platform, powered by the Analytic Information HUB. The solution is designed to cover end-to-end, data-driven finance processes and it’s underpinned by explainable ML.
We’ve addressed the finance office’s need to fill the gap between real and actionable data by leveraging the Analytical Information Hub, our powerful information-centric data hub that enables you to harness vast amounts of granular financial and operational data.

With explainable ML in CCH Tagetik, our customers can extract the genome of their business from data. They're better able to explain the business and run simulations to determine what’s really driving the figures — and the DNA of that makes them unique in the market.

We want our customers —and every single one of those 582 million entrepreneurs — to have access to actionable, accurate predictions with explainable ML. We hope to do this by enabling business insights with automated ML so our customers can get to the finish line faster. By using CCH Tagetik to make data-backed decisions, you’ll build a more data-driven, integrated CPM process from revenues to cashflow.

Share this post!