Think of a world where every fluctuation in the market, every deviation in customer preferences, and every unpredicted occurrence isn’t just noise but encrypted data waiting to be decrypted. To complement the decryption, it seems that data analytics has come into the picture to be a tool in risk management. Data analytics in risk management isn’t considered to be merely about crunching numbers but revolves around converting raw data into actionable insights for influencing decisions. “In today’s data-driven economy, I think data analytics plays a crucial role in risk management for businesses. It can empower businesses to not only personalise customer experiences but also identify and mitigate risks. With the advancements in technologies like artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT), businesses can capture and analyse vast amounts of data, from customer behaviour to system logs. However, with increased reliance on data comes new risks,” Sarfaraz Hussain, business lead – BFSI, India, Infobip, a global cloud communication platform, told FE TransformX.
As per numbers provided by Statista, a market intelligence platform, the extent of worldwide data generation will cross 180 zettabytes by 2025. With digital technologies coming in, corporations can make use of data analytics to ensure goals correlate with their strategic needs. Data provided by Ernst & Young (EY), an audit firm, stated that data analytics for risk management involves five steps, which include risk identification, which involves merging of internal and external data elements to recognise developing risk paradigms, risk assessment and prioritisation, which means to grade risks to guarantee focus on necessary sectors, risk response and mitigation, which ensures choosing of the best possible risk mitigation alternative after assessment, risk monitoring, which helps in supervision of trends around data models related to risks, and risk reporting, which provides real-time understanding of risks while maintaining records of previous results. Talking about the different risk management strategies, data analytics can contribute towards optimisation models, which directs data-backed resource deployment towards risk mitigation, scenario analysis, which recreates likely circumstances to calculate the effectiveness of different risk mitigation applications, and personalised answers, which supplies custom-fit replies towards certain market conditions.
“I believe the role of data analytics in risk mitigation has become paramount, enabling organisations to make decisions based on data-driven insights. By leveraging advanced analytics techniques, such as predictive modelling and ML, we can anticipate threats and take measures to mitigate them. From a business perspective, data analytics is considered indispensable in risk management as it helps organisations identify, assess, and prioritise risks. Companies that leverage data analytics in risk management can gain an edge by minimising disruptions, maximising opportunities, and safeguarding their reputation,” Yuvraj Shidhaye, founder and director, TreadBinary, a digital application platform, mentioned.
According to Mordor Intelligence, a market intelligence and advisory firm, the international risk analytics market magnitude is predicted to clock $47.48 billion in 2024 and $86.89 billion by 2029, at a 12.84% compound annual growth rate (CAGR) for 2024-29. The firm also mentioned that in terms of organisations being subjected to regulatory pressure from the cyber purview, they are coming under guidelines such as the Payment Card Industry Data Security Standard (PCI-DSS) and the National Institute of Standards and Technology (NIST) Cybersecurity Framework. While the PCI-DSS is a security policy for corporations working around credit and debit cards, the NIST is meant for enterprises to be able to handle cybersecurity-oriented risks. Reportedly, in terms of sectors, risk analytics is growing in demand, especially for banking, financial services and insurance (BFSI) and logistics firms, and cloud computing has emerged as a pathway to introduce risk analytics for these organisations. Insights from a study conducted by Grant Thornton, an accounting company, found that 85% of the respondents highlighted the need for their bank-based information and information management programmes to have extra efficiencies for tapping into their full potential, while 82% emphasised on diverting the measures towards their institution’s risk analytics. Global Association of Risk Professionals, a non-profit organisation, has estimated global spending around risk analytic-oriented applications to reach $96 billion for banking, capital, and insurance markets.
“In the realm of risk management, business process co-pilots, underpinned by advanced generative AI technology, seem to be redefining efficiency and strategic foresight. These innovative co-pilots can excel in extracting and analysing critical information from text sources, such as reports, documents, emails, social media, and chat transcripts, thereby automating and significantly streamlining risk assessment processes. This automation can lead to a reduction in manual processing time, often by over 70%. The core of business process co-pilots’ effectiveness in risk management seem to lie in their ability to understand intents, recognise entities, and perform sentiment analysis,” Sagar Mahurkar, vice-president, Findability Services, an AI product and solution provider, concluded.
