4 Steps to Unlocking Advanced Risk Analytics in Finance Using Bank Data

Jun 29, 2021 | Blog, GIS in Agriculture

4 Steps to Unlocking Advanced Risk Analytics in Finance Using Bank Data

Risk analytics plays a key role in the financial sector, making it possible for banks and other institutions to better identify and mitigate material risks. In the wake of the 2008 financial crisis, the Securities and Exchange Commission “identified a number of risk management practices that enabled some global financial services organizations to withstand market stresses better than others.”

 

Over a decade later, as climate change threatens the stability of the financial sector, investors and regulators alike are eager to avoid repeating the same mistakes. GIS technology empowers financial institutions in protecting their assets and investments from the physical risks that are material to their business and borrowers.

 

This article will explore ways that geospatial solutions can improve risk analytics in finance and help to build a more resilient financial sector.

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Bank Data Connected With Everything Else

According to a report by the non-profit Ceres, “more than two-thirds of the average loan portfolio, or $553 billion, is exposed to transition risk,” or risk that is associated with the transition to a net-zero or carbon-neutral economy. As the Bank of the West explains, this requires a different approach to decision-making than that used in the past:

“Historical information on economic and financial performance is much less relevant to assessing climate risk because the risk depends on future changes in the climate and operating environment.”

They propose taking a scenario-based approach to risk analytics that takes into account everything from credit risk to supply chain risk. Since many of these risks are inherently geospatial in nature, they require a detailed understanding of how different aspects of financial risks intersect. 

 

What will really set financial institutions apart is being able to understand their data in the context of both external and internal risk factors – again, geospatially.

 

 

Why Having the Right Portfolio Analytics, GIS Tools, and Spatial Representation of Risk Matters

Ceres points out that financial institutions don’t have to feel trapped in outdated strategies: “Methodologies for stress testing and scenario analysis are robust enough to be widely used and provide a starting point for the urgent work of conducting more granular risk assessment at the client level.”

This process starts with having the right portfolio analytics and GIS tools to be able to identify and represent risk in an easy-to-understand format. Financial institutions can use this information to drive internal decisions and to disclose relevant data to other stakeholders. Ultimately, this can help companies meet their ESG (environmental, social, and governance) goals and become more attractive to investors.

For example, agricultural banks, lenders, and other stakeholders can follow these four steps to unlock advanced risk analytics in finance using bank data:

 

1. Integrate 

First, financial institutions can use advanced API (Application Programming Interface) integrations to improve their data collection process. This allows different parts of an institution, and even external stakeholders, to access appropriate datasets, without having to fully integrate their systems or share proprietary data with each other.

Many cloud-based risk analytics platforms come with these integrations built-in, but stakeholders can also perform data uploads manually using standardized templates. This makes it possible to integrate loan, appraisal, sales, and other data sets into a secure data warehouse specifically designed for ag lenders and investors.

 

2. Map

Next, financial professionals can view their data in a map-based format to make it easier to identify risks as they really are: spatial and dynamic. The right risk analytics tools can geospatially map lender and bank data so that users can better understand how these risks vary by location and explore connections between different types of risk. 

For example, agricultural lenders must pay close attention to geospatial data because water rights, regulations, and drought risk vary so widely from one basin to another. By relying on traditional approaches to decision-making that focus on the financial risk of the borrower, ag lenders are leaving out a key component of risk analytics.

With a map-based GIS tool, financial institutions can perform a more thorough water risk assessment and protect their portfolio against physical and material risk. They can zoom in on a specific parcel of land, or zoom out for a macro view of how water stress is likely to impact different regions of the country.

 

3. Contextualize

Once data has been collected in a map-based format, it is crucial that it is contextualized with additional information such as parcel-specific water risk and land data. GIS tools allow users to toggle on and off multiple layers, making it easy to add additional information when necessary and hide it to avoid confusion.

Private companies are feeling pressure to disclose climate risk and ESG data, creating the need to integrate this information into risk analytics platforms and assess the entire supply chain.

AQUAOSO automatically adds context to customer data with proprietary water risk data, parcel and land information, and other curated data sets.

 

Some of the data contextualization data include:

  • Soils
  • Fire maps
  • Floodplains
  • Flood risk zones
  • Critical habitats and endangered species zones
  • Water quality
  • Regulatory boundaries
  • Regulatory restrictions
  • Water costs
  • More…

Ultimately, ag lenders can’t truly quantify a borrower’s financial risk without accounting for regulatory and environmental factors, which should be represented geospatially.

 

4. Analyze

Finally, ag professionals can run advanced analytics, print out PDF reports, and view dashboards at the parcel, loan, borrower, or portfolio level. This makes it possible to share data with other stakeholders and make sure that everyone is operating from a single source of truth concerning transition risk and overall portfolio risk.

The future of risk analytics in finance will require a collaborative approach, in which all parties work together to mitigate risks related to water stress and climate change. By making better lending decisions and by supporting borrowers as they work towards more resilient farming practices ag lenders can reduce risk exposure in their own portfolios and across the entire financial sector.

 

 

The Bottom Line

Climate change poses the greatest risk to financial stability since the financial crisis of 2008. By incorporating risk analytics and GIS tools into their decision-making process, lenders, Farm Credits, and banks can save time and money and build resilience. By improving operational efficiency and strategic decision-making, they can lead the financial sector into the 21st century.

 

Cloud-based GIS solutions like AQUAOSO can be especially helpful for ag lenders, Farm Credits, and investors who want to expand their understanding of climate risk as it pertains to their portfolios. The GIS Connect tool is a map-based platform, purpose-built for just this.

 

Contact the team at AQUAOSO for a free demo, or visit the resources page to learn more about this and other water security topics.

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