Predicting the future of compliance with analytics

Predicting the future of compliance with analytics
Predictive analytics: A powerful tool helping companies in making better business decisions through digital transformation

In today’s business landscape, we are seeing a greater number of organisations investing time, effort, and money into digital transformation, with the ultimate goal of driving fundamental change.

When it comes to digital transformation, there is no one-size-fits-all approach. Each organisation's journey will be unique, depending on its specific needs and objectives. For example, one company might use AI or cloud computing to improve its customer experience, whereas another might redesign its supply chain to leverage machine learning. Another company may even use predictive analytics (PA) to predict customer demand in advance and adjust its production accordingly.

According to a Gartner study, which was conducted from October 2022 through April 2023 among 200 global corporate strategy leaders, 79% believe that AI and analytics will be critical to their success over the next two years. In the report, strategists have stated that on average, 50% of strategic planning and execution activities could be partially or fully automated, however only 15% currently are.

The benefits of predictive analytics in a business environment

Although predictive analytics has been around for decades, it is only now that we truly recognise the major benefits that this technology can bring to businesses. By using algorithms and machine learning to analyse data and predict what is likely to happen, companies are provided with a more complete picture of a situation which in turn helps them to make better decisions.

Bret Tushaus, VP of Product Management at Deltek, says: “By understanding likely outcomes, employees can be more empowered to make decisions and to focus more of their time and energy on the human element side of projects, such as value adds that make the business stand out from the competition.”

Due to its versatility, predictive analytics can be used across a variety of industries, from manufacturing and supply chain to financial services, insurance, marketing, and human resources; each utilising it as a tool to improve business performance.

Peter Wood, CTO at Spectrum Search, says: “In today's competitive business environment, foresight is as valuable as hindsight. As someone deeply involved in AI innovation and technology that optimises internal processes, I can attest that predictive analytics has become a cornerstone in contemporary business strategies.

“Businesses aren't just looking to make sense of their current data; they aim to predict future trends, consumer behaviours, and potential risks. Predictive analytics allows companies to not just react but proactively adjust their strategies.”

Types of predictive analytics models

According to Investopedia, there are three common techniques that are used in PA: Decision trees, neural networks, and regression. 

Decision trees, which help in the understanding of a person’s decisions, are the simplest of models, being easy to understand as well as extremely useful when needing to make a decision quickly. They work by placing data into different groups based on specific variables, and as the name suggests, look like trees, with the branches representing the possible choices and leaves representing a specific decision.

Regression is the model that is used most often in statistical analysis when determining patterns in large datasets where the inputs and outputs have a linear relationship. It works by finding a formula that best fits the data, which can then be used to predict future outputs based on new inputs.

Lastly, neural networks have been developed to imitate the way in which the human brain works. This model can handle complex data relationships using artificial intelligence and pattern recognition. It is useful for tasks such as making predictions, dealing with large datasets, and finding relationships between inputs and outputs when no formula is known.

Predictive analytics in business compliance 

Today, many companies are investing in PA as part of their compliance strategy. By using data and algorithms to predict future outcomes, it can help companies to identify risks more accurately and efficiently, monitor customer and employee behaviour, and fulfil regulatory responsibilities.

Some of the applications of PA for compliance in business include banks implementing it to identify customers who may pose a liability, healthcare professionals detecting at-risk patients, and insurance companies spotting potential fraudulent clients.

Martin Butler, a Professor of Management Practice at Vlerick Business School, explains: “Compliance is becoming more exhaustive as regulators expect businesses to comply with more complex requirements and provide proof of this compliance. With increased digitisation in internal processes and customer and business partner interaction, the surface area for compliance breaches has increased substantially. 

“Predictive analytic data models and algorithms enable quicker and more accurate identification and monitoring of the processes and data movements to quickly identify potential non-compliance and create immediate responses.”

Predictive analytics are not a crystal ball for market trends

While predictive analytics tools can be extremely beneficial to a company, there are understandably a few drawbacks that business leaders need to be aware of. Although PA can accurately predict some human behaviour, not all can be foretold. 

Wood explains: “Predictive analytics is not a magic wand. One of the primary challenges is data quality and integrity. Bad data in equals bad insights out. Another issue is the 'black box' nature of some predictive models, which can make it difficult to ascertain why a particular prediction has been made. 

“This opacity can be problematic in sectors where explainability is crucial, such as healthcare or criminal justice. Finally, there's the ethical dimension. As predictive systems get more sophisticated, there's an increased risk of algorithmic bias which can perpetuate societal inequalities.”

As Butler decribes, vendors bring technology and some of the skills required, but organisations must provide the business context, skills and insight to gain full value from predictive analytics. They must take ownership of predictive analytics projects and ensure multi-skilled teams with excellent business insight in the application domain steer the initiative. 

PA models are complex and require specialised skills to build and maintain. These skills are in short supply as well as being expensive, which is why many companies cannot afford to invest in them internally, however, a much more cost-effective way to access the benefit of PA is off-the-shelf PA tools.

Are we able to predict the future of PA technology?

PA’s overall goal is to make predictions about future events, before using those projections to improve decision-making. Businesses of all sizes are beginning to adopt PA as part of the business strategy and we are starting to see the early benefits in terms of additional time, resources, and business efficiency. Because of this, investment in PA technology is expected to increase even more in the coming years.

Tushaus says: “Integrating predictive analytics across operations and combining predictive power with human experience will drive business value. As a result, this technology will not only help prevent budget and schedule overruns, it will also lead to better line of sight into project success making it more likely for projects to proceed in the first place. This can, in turn, lead to better industry performance.

“As we approach 2024, every minute counts and predictive analytics hold the opportunity to give businesses back time and resources – a crucial business investment to maintain pace with competitors.”

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