Risky business: location-based intelligence tackles credit management

Change is a constant in the financial world. But over the last decade, the pace and magnitude of change have introduced new imperatives in the management of credit risk. As regulators continue to increase the breadth and depth of regulatory oversight, companies are wrestling to address growing requirements while protecting profitability. Looming over these policy-level issues is the accelerating impact of climate change: environmental transformation has reached a tipping point where adaptation is a necessity and catastrophic events are the new norm. For financial services businesses, pressure to master compliance requirements, combined with the need to develop products and services that address increased environmental threats is adding pressure – and urgency – to the already-intense competition in the financial marketplace. To respond, many financial institutions (FIs) are developing new process around risk assessment and credit management, enabled by a range of automated tools, including location-based intelligence.

Aftershocks from the crisis of 2008 have had a profound impact on the financial community. At a high level, this resulted in enhancements to regulatory banking frameworks, such as Basel III, which aimed at bank reform through greater transparency into operational practices, improved supervision and monitoring of credit risk. At the individual FI level, this reform has translated into renewed emphasis by regulators on consistent and full reporting. According to SWIFT, a cooperative organization that provides financial data exchange on behalf of 10,800 global members as well as industry insight, one of the common thread in investigations and penalties imposed by financial regulators is the lack of complete and accurate information in reporting.[1] But issues with data integrity extend beyond regulatory exposure, to affect business’ bottom line in a number of ways: as SWIFT researchers point out, when data is missing, risk engines have to use conservative estimates when determining the required regulatory capital, limiting that which is available for more profitable investment opportunities. Additionally, when a proper framework is lacking, activities to assure data quality can consume significant resources: a KPMG financial advisory, “Managing Credit Risk; Beyond Basel II,”[2] has found “Without a proper data management framework, quality assurance measures may easily account for 75 percent or more of the time available to generate a report.” On the other hand, with measures in place to ensure data integrity, financial service organizations can address regulatory reporting requirements, optimize in-house risk management and improve operational cost savings.

Another key impact on credit risk management is the unmanageable – the effect of climate change on value assessment of businesses, their physical assets and supply chain. If the precise cause of this transformation is still up for debate in some political circles, there is now broad consensus in the scientific community on the magnitude of change. As the figure below from the Intergovernmental Panel on Climate Change shows, in each of three emissions scenarios modeled with varying degrees of economic growth and use of clean technologies, rise in annual average global temperatures (relative to 1980-1999) is set to unleash “inevitable climate change” and with it, business risk across a number of sectors.

Annual average global temperature increases to 2100 under different greenhouse gas emissions scenarios relative to the 1980-1999 baseline. The B1 scenario represents a future where clean and efficient technologies are used. The A2 and A1B scenarios describe future worlds where economic growth is rapid and the environment has lower priority. [Source: IPCC, 2007].
Annual average global temperature increases to 2100 under different greenhouse gas emissions scenarios relative to the 1980-1999 baseline. The B1 scenario represents a future where clean and efficient technologies are used. The A2 and A1B scenarios describe future worlds where economic growth is rapid and the environment has lower priority. [Source: IPCC, 2007].
 According to Barclays’ Environmental Risk Management & Acclimatise group[3], changes in weather patterns associated with increased concentrations of CO2 (a two or threefold increase by 2100), may introduce material risk for businesses on a number of levels: there may be physical risk to fixed assets arising from storm damage or flood, increasing scarcity of natural resources such as water or other raw materials may impact cost and/or supply chain, while patterns in demand for goods and services may shift due to increased extremes of temperature. Asset values may be affected, weakening a company’s balance sheet, and temperature fluctuations can require a change in operational or working practices or reduce demand for a company’s products or services. Going forward, the impact of average temperature rise and increased risk of heat waves, mean sea level rise, increased storms surge heights, wave heights, coastal flooding and erosion, decreased seasonal precipitation, increased risks of drought, subsidence and wildfire, increased seasonal precipitation and more rapid snow melt, increases in heavy precipitation events leading to increased risk of flash floods and soil erosion, and increased storm intensity and frequency is a critical factor that must be taken into account when valuing a business’s assets, operational stability, market potential and obligation to local communities and the natural environment.

To manage increasing risk complexity, many institutions are turning to process automation that can ensure accuracy and compliance with regulatory reporting, while taking advantage of data to increase visibility into the business. “With margins under pressure” from increased competition in difficult economic times, SWIFT noted, businesses need to understand how risk is distributed, where profits originate, how capital is structured, and how to assess and respond to this information. At an operational level, demand is also growing for the “commoditization” of complex, structured products based on solid reference data that can act as an enabler of business intelligence tools for processes, such as credit assessment – where manual processing of data and applications leads to inefficiency, error, cost, and ultimately acts as a constraint on growth. In addition, automation can provide decision support through consolidated views of client and/or market risk exposure, as well as enhanced ability to deliver real time processing of applications, which is an increasingly pervasive customer expectation.

So how do location-based intelligence solutions underpin this complex interplay of new reporting requirements, multiple and highly variable risk factors and new opportunities offered through process automation? Defined by Steve Sigal, Vice President of Product Marketing for DMTI Spatial, as “business intelligence plus geography,” location-based intelligence systems provide real time, accurate information that has been verified by a several geospatial services which can be integrated into back end systems to support decision-making. For example, in the mortgage field, correct and complete location information can help financial services firms meet reinforced mortgage rules introduced recently by the Canadian Department of Finance and the Office of the Superintendent of Financial Institutions, while ‘proximity to peril’ such as flooding or other environmental threats can help in individual mortgage adjudication or in the development of pricing based on credit risk assessment for areas or regions that may be especially prone to risk associated with catastrophic weather events, or shielded from nearby threats. With a consolidated view of risk associated with a particular area or property, which can be integrated with third-party or other weather, demographic or economic data, the underwriter can better assess potential mortgage portfolio exposure in the event of a natural disaster or economic downturn. And armed with comprehensive location information, the institution may better positioned to manage two key credit management challenges – mortgage fraud, estimated at $400 million in Canada by Equifax in 2012, and mortgage default, which is a factor across the $1 trillion residential mortgage credit market in Canada.

Another example, drawn from the insurance industry, illustrates how location-based intelligence can be implemented within an organization not only to support credit analysis, but to improve operational performance. Intact Financial, a provider of home, auto and business insurance, has partnered with location intelligence solution vendor DMTI Spatial on the creation a real-time application that can provide location information to help underwriters evaluate accumulation and flood risk presented by new and existing policies during the underwriting process. The project was completed in two stages. The first encompassed cleansing of address data on existing policies and the automation of correction processes for the input of data from new policies to ensure data quality, followed by geocoding to link addresses to geographical location, a process designed to help underwriters assess and differentiate location-based risk, determine appropriate premiums, and consolidate new and existing policies to highlight areas of concentrated risk. Phase two involved creation of visualization tool that interfaced with existing Intact legacy underwriting systems to introduce new risk evaluation layers accessed through mapping. In the map platform, clicking on a property icon yields street map data, existing Intact risk layers from adjacent properties with their underwriting characteristics, flood zones, including the associated depths for each zone, a data layer outlining elevation and proximity to water that helps an underwriter determine if the property sits above or below the level of the nearest water source, as well as distance measurement and zoom capabilities. By integrating internal data – building information, total insured value of an address and claims activity – with external spatial data – flood zones, elevation – the solution has helped to develop rating services specific to an individual property or area. At the same time, the automated delivery of geocoded information enables real-time delivery of granular insight into environmental risk as well as policy information to allow better segmentation for pricing and underwriting.

For Intact, the system offered a number of operational advantages, including a uniform tool for underwriters, efficiency gains through system automation of rates, deductibles and minimum premiums, economies of scale since the platform, including geocoding and geomapping can be accessed across a number of applications, secure access to third-party databases, as well rapid application delivery through the use of pre-rendered map tiles. As a measure of new efficiencies, Intact has estimated a 15 percent reduction in processing time for the quoting process (or a 6 month ROI on the technology), a measure of Intact’s new ability to better compete in insurance markets. For Intact, the project has delivered an additional advantage – nomination for a 2015 Insurance-Canada Technology Award for its DMTI-enabled “Risks in their (Proper) Place” solution. The recognition, like the system itself, is effective on two levels: it recognizes the importance of location-based intelligence in managing risk in financial businesses, and highlights the benefit of systems that connect spatial information to existing processes and emerging requirements.

 

[1] SWIFT and Deloitte White paper. Growth, risk and compliance: The case for a strategic approach to managing reference data, April, 2012.

[2] KPMG Financial Services Advisory. Managing Credit Risk. Beyond Basel II, October 2007.

[3] Bray, C., Colley, M. and Connell, R. Credit risk impacts of a changing climate, Barclays Environmental Risk Management and Acclimatise, 2007.

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