top of page

Blog Post

Single Risk View

What is Single Risk View (SRV)?

To understand what is SRV, it will help to understand the history of Single View of things in the business world and how it has evolved. Single View of things range from assets, data, customer etc. and the list goes on.

In the last decade, Single Customer View (SCV) has been the most commonly tackled the challenge in data-centric organisations. Large organisations that have inorganically have different systems with customer data and many with the same customer records. This scenario has led to poor customer service, operational overheads, inability to improve the bottom line. An SCV is an aggregated, consistent and holistic representation of the data known by an organisation about its customers. An SCV provides companies with the ability to monitor customers and their communications across every channel. The apparent benefits of this include much-improved client service levels, better customer retention, higher conversion rates and an improved overall customer lifetime value. Organisationally this will also lead to better communication between Traditionally separate teams and a more concerted approach to customer service. *

Risk functions can draw parallels from SCV because of the nature of the challenge and impact to the business. Risk functions struggle to have a single view of the risks (CREDIT RISK, MARKET RISK, LIQUIDITY RISK, and OPERATIONAL RISK) that impact the business as a whole. The Chief Risk Officers and risk functions biggest challenge is the constant fire fighting to understand holistic risk profile. This is mainly due to the siloed and manual nature of risk capture mechanism, storage of data in disparate systems, very tedious and old school manual mechanism to identify and create risks.

Single Risk View (SRV) - is a solution to enable Chief Risk Officers to understand the risk profile of the entire company at any given point in time and take impactful, timely decisions.

We tested several hypotheses with Chief Risk Officers in large banks:

1. All data-driven enterprise risks are not documented in a structured and consolidated ”single” view

2. Our risk function prefers to have the ability to AUTO-DISCOVER and AUTO-CREATE the risks

3. Risk functions will need to look into multiple sources for data supporting the risks and fixing a material cost

4. Risk functions make the risk-mitigating decision typically with few or no decision options

5. Risk functions make decisions based on experience, intuition but by gathering supporting data for a decision

6. Risk functions will prefer to have a dynamic dashboard of Risks with parameter showing risks volatility over multiple factors

7. Risk functions will prefer to have AI predicted the best action based on options for the risks identified

8. Risk functions will prefer to have steps linked to a workflow with other relevant company teams to work on risk mitigation

9. The company has to balance prioritising automation of (IT) development over the quantum of risk and value

10. Priority IT development projects within the risk management/valuation area take time to live, and until then the firm carries the risk and cost of managing the risk

11. No tool can be pointed/configured out of the box to manage complex instruments or portfolio items

12. Ability to assess the interconnectivity of the risks related to a customer and their customers (direct or indirect). Example: If a bank holds a AAA bond by Google and at the same time, they offer revolving credit facility (RCF) to 5 different companies that work with Google. If there is an adverse judgement where by Google has to stop a service these companies benefit from potentially the security of the RCF could decrease from AAA to CCC or even underperforming.

How to implement a Single Risk View?

Single Risk View (SRV) is difficult to implement by using the same methods as Single Customer View (SCV) because of the different data types involved. Single Customer View involves only customer data which is static data that does not change over time, but Single Risk View involves static data (Customers, Product etc), transactional data (Credit transactions, Thresholds), and reference data (Credit ratings, FX etc).

Single Risk View (SRV) requires a blend of automation, AI on the enterprise data to successfully deliver the value and impact. Critical Components of a Single Risk View are:

1. Automated Data Integration

2. Auto-risk discovery and auto-generation

3. Rules management

4. AI to identify the probabilities of risk and robo-adviser for the mitigation action

Below is an illustrative example of a Single Risk View (SRV) solution:

1. Single Risk View of a bank

2. Auto-discovery and auto-generation of risks based on thresholds

3. Heat map of auto-discovered and auto-generated risks

4. Auto-created risk detail with probability (AI-driven), criticality, implication and robo-adviser (AI-driven)

5. Rules management (1/3)

5. Rules management (2/3)

5. Rules management (3/3)

6. Data management (1/3)

6. Data management (2/3)

6. Data management (3/3)

What is the need for a Single Risk View now? The answer simply is that opportunities outweighs the threats and overheads.**

Key benefits of a Single Risk View (SRV) are:

1. Improve operational efficiencies

2. Proactively address regulatory compliance such as Basel, Solvency etc

3. Turn the risk function from a cost centre into revenue realisation unit by de-risking non-performing assets

4. Integrated data is treated as an asset for BAU and can be used for other use-cases

5. SRV AI models can trained and re-used in other use-cases for a different data sets

Single Risk View (SRV) solution and the term "Single Risk View" are proprietary to Derisk360. To discuss how to implement Proof-of-Concept of Single Risk View, please get in touch.

** McKinsey Global Institute

317 views0 comments

Recent Posts

See All


bottom of page