Risk managers in today’s disruptive and tech-driven era face a fundamental conundrum: Having to do more with lesser resources.
This was the key message that was emphasized at the official launch of Marsh & McLennan Companies’ (MMC) Targeting a Technology Dividend in Risk Management report that was held in our Marsh office in Singapore on January 25, 2018.
The report- the product of the very first collaboration between MMC’s Asia Pacific Risk Center and the Pan-Asia Risk and Insurance Management Association (PARIMA), a leading professional association for risk and insurance managers– is based on key findings and results from the Emerging Tech in Risk Management Survey of 2017 of over 130 risk professionals cross-industries. It also comprises of business applications, case studies, and perspectives from across Marsh, Oliver Wyman, and our partners.
Three major emerging technology levers
According to The Emerging Tech in Risk Management Survey 2017, two-thirds (67%) of the respondents cited that cost and budgeting concerns are the most significant obstacles risk managers face in the upgrading and digitization of the Risk function.
Complex interplays between varieties of global trends are heightening uncertainty across the risk landscape, which include macroeconomic headwinds and shifting socio-political trends amidst the rapidly evolving technology landscape.
Only about 17% of the survey respondents stated that risk management is advancing a central role in making or informing business decisions. However, as regulators start to push for greater risk oversight for both financial services (FS) and non-FS companies, it is now the time for risk managers to push for more investment and support from the rest of the C-suite.
As such, technology advancements provide potential solutions for risk managers to improve effectiveness and spark business insights. In particular, the risk profession may reap significant technology dividends by leveraging innovations in three key areas:
- Data: Building a rich risk database of real-time big data from new sources (e.g. cloud accounting, application programming interfaces (APIs), social media, geolocation software, etc.)
- Analytics: Generating forward-looking risk-informed insights (e.g. machine learning, predictive forecasting)
- Processes: Digitizing the risk function to boost efficiencies and reduce costs (e.g. robotic process automation for risk monitoring)
Analysing vast datasets allows firms to unlock valuable insights while self-learning analytics algorithms can predict and prevent risk events. In addition, process technology can automate standardized tasks and increase efficiency. Targeting a technology divided with these technologies will allow risk managers to solve for newer uncertainty with the same capacity.
Despite plans for digitization, most Risk functions are still using traditional technologies in risk management. Risk professionals were also surveyed on the technologies they were currently using, and what they were planning to use.
The survey findings deduced that spreadsheets (76%) are the most commonly used tool among all technologies, closely followed by risk assessment (52%) and tracking (38%) tools. Fortunately, risk professionals are hopeful and expect large gains from emerging technologies, such as predictive analytics (26%) and data engineering (22%), as compared to traditional technologies.
Piloting an advanced country credit risk monitoring methodology
Amongst multiple real-world applications of technology innovations, a case study on leveraging risk analytics was also presented by Gaurav Kwatra, Principal (Finance & Risk Practice) at Oliver Wyman.
In 2017, a global institution, with the support of Oliver Wyman, enhanced its strategic foresight capabilities by developing a sentiment-based modelling tool that dynamically mapped and tracked country risk from its physical supply chain. First, machine-learning algorithms were developed to process real-world incident and natural language data from multiple sources, including news events, financial databases, third-party vendors, and social media platforms. Next, the model extracted trackable sentiment-scores to calibrate country-level risk limits and other early-warning signals and mechanisms, creating a market-scoring system.
Overall, the global institution benefited from significant upgrades to its strategic foresight capabilities, incident response times, and critical business processes.
In addition, its risk coverage expanded to include even political and reputational risks, which are typically challenging to quantify but are now covered to holistically contribute to its enhanced strategic steering and risk management framework.
Harnessing telematics data in vehicle insurance
Besides risk analytics, harnessing the full benefits from digitizing the Risk function also requires a complete digital transformation in data management. Celine le Cotonnec, Chief Data Officer, AXA Singapore, further elaborated on AXA’s telematics data and risk management framework which was among the report’s case studies.
The ability of insurance providers to use data for an expanding suite of purposes is crucial for corporations looking to mitigate risk as well. In the field of Fleet management, AXA Insurance Singapore partnered with the main Fleet Management Solution (FMS) providers to segment drivers based on driving style information (such as acceleration, harsh braking) and allocate risk scores.
A safe driving program for drivers is then developed, using their personal data to show them what they can do to improve their driving. This reduces accidents and fleet management companies’ loss ratios, helps provide savings on insurance premiums and prevents fraudulent claims.
Digital horizons of change and quick-wins for risk managers
Digitizing the Risk function may be an extremely challenging task, but the forward-looking insights from the unprecedented of real-time big data and advanced analytics afforded by the emerging technologies are not to be missed.
There are three horizons of change across the complexity scale for risk digitization as Risk functions undergo this transformation: Level 1 -- a traditional risk function optimisation, Level 2 -- a progressive risk function foundation, and Level 3 -- a fully digitized risk function.
While most surveyed respondents suggest that their firms have not fully explored these horizons of change, many have ambitious plans to embark upon the journey. For example, slightly over a-third responded that they were at no level of digitization, and 14% of respondents have no plans to embark on any digitization at all. Encouragingly, 57% of respondents are currently at the first level of digitization, while 48% are planning to reach the second stage of digitization.
The following summarizes briefly the five practical steps for risk managers to kick-start the digitization of their risk functions:
- Launch ‘quick wins’. Identify longer-term efficiency gains based on the digital risk activity map
- Scan the competitive landscape. Understand current positioning in comparison to peers
- Define digital ambitions. Outline strategy, position, and vision for the future of risk management
- Align regulatory strategy and relationship. Monitor regulatory changes on emerging technologies
- Establish recruitment strategy. Anticipate long-term human capital requirements
It is evident that the long-term benefits of these technologies widely outweigh the initial costs; hence it is crucial for risk managers to forge ahead with the digitization amidst challenges. In light of technological advancements, the risk management operating model must evolve to support the shift; risk professionals today have to do more with less.
