Data science has been vital in enhancing risk management operations in recent times. With cyber attacks, including phishing and ransomware, on the rise since the Covid-19 pandemic took hold, managing and mitigating the effects of such incidents, with the aid of network visibility, is key to business continuity. Additionally, there are IT outages and insider threats to contend with, which also require a strong risk management strategy.
In this article, we explore how the future of data science’s role in risk management initiatives will take shape.
With incidents that can bring operations to a stand-still becoming more diverse, it’s vital that those risk management measures are as agile as possible to avoid being caught out. Data science can help businesses to better analyse short-term and long-term trends, and respond to possible risks and disruption quickly, and this is set to be focused on more going forward.
“Whether in marketing, sales, demand, pricing or operations, the key to risk management is not only in spotting the potential risks, but in understanding their likelihood, scale and impact and then reacting accordingly,” said Matt Andrew, partner & UK managing director of Ekimetrics.
“In retail, for example, we’ve seen the impact of not having a thorough enough understanding of market, category and consumer trends and risks with mitigations in place soon enough to react in the face of a market-changing pandemic. For the likes of Arcadia Group and Debenhams, factors such as the high cost of brick and mortar stores and a failing offer, including poor e-commerce, became increasingly impossible to deal with. Those that had already begun to invest in this area of data science will have had a better chance to regroup quickly and make better decisions, from big pivots to the ability to capitalise on micro opportunities.
“By understanding the potential range of outcomes and how they interact through data analytics, businesses can support greater agility in their decision-making about where and how to invest, and help to future-proof against other risks that are yet to emerge.”
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Minimising reconciliation error through automation
A key aspect of data science that has a bright future is automation. This decreases strain on data scientists while speeding up processes, and when it comes to mitigating risks, automation can minimise errors when it comes to data reconciliation — the movement and alignment of critical company data between systems.
Douggie Melville-Clarke, head of data science at Duco, explained: “As businesses move towards making more data-first decisions, the emphasis on data automation is growing, with companies automating as much of the data reconciliation process as possible to speed up process, help businesses scale and crucially mitigate risk.
“Data reconciliation has traditionally cost financial firms significant sums of money through man hours and regulatory fines. Automation takes away the human error element from data reconciliation. Manual tasks can often become tedious to a human brain leaving room for error, but a computer can’t get bored or show up to work tired. It’s consistent. And this consistency is crucial when dealing with large datasets.
“Repeatable tasks can be delegated to a computer to handle more efficiently – and with a lower error rate – freeing up the workforce to do jobs that add more value to the business, such as new product offering or adapting to regulatory changes.
“Data automation platforms also enable businesses to get a full view of the data transformation process, end to end. Through automated data lineage, businesses can track the cleansing and manipulation processes the data undergoes, giving them a holistic view of the data in a structured way, as opposed to an unstructured one. This aids with error spotting and reporting, both internally and to regulatory boards.”
Handling more data, and looking to the future
According to Trevor Morgan, product manager at comforte AG, the value-add that data science is set to bring to risk management in the near future is two-fold: the ability to manage more data in one go, and looking to the future rather than past events.
“Enterprise data is growing nearly exponentially, and it is also increasing in complexity in terms of data types,” said Morgan.
“We have gone way past the time when humans could sift through this amount of data in order to see large-scale trends and derive actionable insights. The platforms and best practices of data science and data analytics incorporate technologies which automate the analytics workflows to a large extent, making dataset size and complexity much easier to tackle with far less effort than in years past.
“The second value-add is to leverage machine learning, and ultimately artificial intelligence, to go beyond historical and near-real-time trend analysis and ‘look into the future’, so to speak. Predictive analysis can unveil new customer needs for products and services and then forecast consumer reactions to resultant offers. Equally, predictive analytics can help uncover latent anomalies that lead to much better predictions about fraud detection and potentially risky behaviour.
“Nothing can foretell the future with 100% certainty, but the ability of modern data science to provide scary-smart predictive analysis goes well beyond what an army of humans could do manually.”
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Higher regulation of AI
While AI has demonstrated the capability of helping to increase the agility of organisations’ decision making, there is also the matter of higher regulation of the technology to consider, with legislation in the EU being a notable example. To stay compliant, risk management aided by data science is likely to be the way forward.
“Data science and risk management professionals will work hand in hand to ensure risk and governance procedures are at a high standard,” said Theresa Bercich, director of product strategy and principal data scientist at Lucinity.
“AI compliance will be more regulated, as evidenced by the EU creating legislation around this topic. This means that new job titles, positions and people will join the world of AI (which has already started), that will create frameworks for governance and risk.
“The power of AI and the demand for its value proposition is driving significant changes in the technology space including the breakdown of traditional silos and the development of intelligent software deploying data in a productive manner.”
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