Organizations face mounting pressure to improve operational efficiency and drive business performance and profitability. Volatile economies, evolving regulatory frameworks, and threat agents that penetrate sophisticated detection tools demand more stringent measures. However, many organizations still rely on traditional and manual processes to manage risks, monitor fraudulent transactions, and investigate incidents that have the potential to cause irreparable reputational damage and financial loss. Forward-looking enterprises have started reimagining their processes, ridding their system of guesswork and instinct in favor of more robust analytics tools, monitoring processes, and metrics.
Buffeted by diverse and complex challenges, many organizations are turning to predictive analytics to foresee future trends as well as deal with the business issues at hand. Such systematic analysis and interpretation of data will help them maximize productivity, reduce wasted efforts, deal with uncertainties (e.g., product failure), and drive profits. These new approaches have the potential to give enterprises a firmer grip on their fast-growing big data.
Big data analytics and predictive models have entered a period of significant technological maturity, becoming more powerful in terms of data aggregation and analysis, and more widely available than ever before. Simply said, it’s about proactively managing risk. Predictive analytics provides enterprises with a deep and accurate understanding of how and where fraudulent transactions originate. They also can use predictive analytics to track fraud patterns in real time and step up to meet the challenge from new types of attacks.
Risk and Fraud
Predictive analytics is now a key component in the risk and fraud management repertoire of banks, especially in the light of recommendations, like Basel, around key risks in banking. Such risks include information theft, hacking damage, third-party theft and forgery. By assembling data points on the customer that are scattered online and offline, data integration tools can help in building a unified profile of the customer. These include data points such as the customer’s name, address, email, negative news about the customer, and even scanning against watch lists of embargoed persons.
Analytics tools can then return a risk score for the profile, which indicates the right amount of risk the customer poses to the bank. Similarly, life insurance companies can rely on data mining, an analytics subsystem, to identify customer groups that present the least amount of risk to the company while reducing the cost of manually assessing risk (underwriting). This is very important because in insurance, people who are most likely to buy insurance are the ones who are most at risk.
Predictive analytics and big data techniques help improve the efficacy of fraud investigations by pinpointing fraud patterns and their consequence on different business units as well as by focusing on areas prone to higher fraud risks. Predictive analytics also helps enterprises reduce duplication and eliminate false alerts resulting from inadequate fraud detection mechanisms.
Predictive Analytics and Reporting
Corporate transparency is a big question in today’s world. By ensuring accuracy and consistency of enterprise data, predictive analytics is helping companies report more of their financial details in an age hungering for more accountability. The 2015 Edelman Trust Barometer, for example, finds that “more than half of the global informed public believes that business innovation is driven by greed and money rather than a desire to improve people’s lives.” It follows that companies possessing the tools that can help them demonstrate more transparency in corporate reporting are more likely to build trust with informed publics.
Enhanced Decision Making
Not content with simply identifying and monitoring risks across the enterprise, some businesses have established governance, risk, and compliance (GRC) programs, which provide more effective risk management and help leadership teams make better business decisions. The GRC-based approach will help insurers aggregate all risk data, increase visibility of the risks that matter most to the organization, and highlight remedial action. Further, it eliminates errors and redundant activities that swell up cost, and helps businesses meet regulatory guidelines.
Your organization could be a home office, small- or mid-size business, or a large enterprise. Size doesn’t matter anymore. Ensuring that accurate and relevant information is available to the top management is key to organizational success. Predictive analytics will ensure that a clear stream of insightful data reaches the top management in a timely manner, rather than losing its way in the bureaucratic folds of the organization, so the leadership can make constructive and well-informed decisions.
Predictive analytics is emerging as a big driver of robust data-driven decision-making at organizations big and small. By embracing predictive analytics, organizations signal that they are ready for the shift from instinct-based decision making, ineffective fraud-detection techniques, and outmoded reporting abilities to a more modern and sure-shot approach predicated on data and designed for better success.
Nanda Ramanujam, Director, GRC Solutions, is a seasoned and successful technology management professional with over 18 years of software industry experience and 10 years in architecting, developing, and delivering reliable and scalable enterprise-class Web applications and messaging services for Audit, Governance, Risk, and Compliance management, and Learning Management Systems.
In his current role at MetricStream, Nanda is responsible for planning, staffing, leading, and designing multiple GRC projects with customers, with a focus on driving value and customer satisfaction. Nanda is also responsible for delivering revenue targets and ensuring project profitability.
Subscribe to Data Informed for the latest information and news on big data and analytics for the enterprise.