Design thinking and Data analytics

Design thinking and Data analytics! .....meeting customer needs, regulatory expectations and delivering shareholder value...

Design thinking is a problem-solving methodology that focuses on understanding and empathizing with users, generating ideas, prototyping, and testing solutions. It involves a human-centered approach to innovation and encourages interdisciplinary collaboration and iteration.

Data analytics-based risk management, on the other hand, is a process of identifying and analysing potential risks and developing strategies to mitigate them through data analysis. It involves collecting and analysing data to identify patterns and trends that could indicate potential risks, and then using this information to make informed decisions and develop risk management strategies.

While these two approaches may seem different, they can be complementary. For example, a design thinking approach can be used to identify potential risks and develop innovative solutions to mitigate them, while data analytics can be used to validate and refine these solutions by analysing relevant data.

By combining these two approaches, organizations can create a comprehensive risk management strategy that is both user-centered and data-driven.  Therefore, you start the process of designing a solution with the ‘end user’ or ‘customer’ in mind. This can enable organizations to better understand their users' needs and preferences, as well as identify and mitigate potential risks in a more informed and effective manner. Furthermore this will support organisations to introduce more transparent ‘controls’ across their customer life cycles which helps the customers to appreciate the value of these controls. Not only that this joined up approach will build better oversight on how potential risks are getting mitigated, where by helping the organisation build a competitive edge through customer trust.

Design thinking , not just a problem solving methodology, but also a game changer - The 5 steps to follow


  1. Empathize: The first step is to understand the problem from the user's perspective. This involves observing, engaging with, and listening to users to gain insights into their needs and pain points.
  2. Define: Once you have gathered insights from users, the next step is to define the problem. This involves synthesizing the information and reframing the problem into a clear and concise statement.
  3. Ideate: The ideation phase involves brainstorming and generating a range of possible solutions to the problem. This can be done through various ideation techniques, such as mind mapping, brainstorming, or role-playing.
  4. Prototype: Once you have identified potential solutions, the next step is to create prototypes of these solutions. Prototyping involves creating low-fidelity or high-fidelity models of the solutions to test and refine the ideas.
  5. Test: The final step is to test the prototypes with users and gather feedback. This involves conducting user testing and gathering data on how users interact with the prototypes. Based on the feedback, you can refine the prototypes or go back to the ideation phase to generate new ideas.


It's important to note that design thinking is an iterative process, and it may involve going back and forth between these steps multiple times before arriving at a solution that meets the user's needs and solves the problem effectively.

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Design thinking as an enabler to deliver robust processes, controls and dynamic oversight

Empathize: The first step is to understand the problem from the user's perspective. In this case, the users could be bank employees, customers, and other stakeholders who are impacted by operational risks. We could engage with them through interviews, surveys, Complaint management systems or focus groups to gain insights into their experiences and pain points.

Define: Based on the insights gathered from users, we can define the problem more clearly. For example, we might identify that there are multiple types of operational risks in the bank, including human errors, system failures, and fraud. We could also identify the impact of these risks on the bank's reputation, financial performance, and customer satisfaction.

Ideate: The ideation phase involves brainstorming and generating a range of possible solutions to the problem. We could use various ideation techniques to generate ideas, such as brainstorming sessions, affinity mapping, or storyboarding. Some possible solutions might include:

 Developing more robust training programs for employees to reduce the risk of human errors

  • Investing in better technology and cybersecurity to prevent system failures and data breaches.
  • Implementing more rigorous fraud detection and prevention measures
  • Establishing better communication channels between different departments to improve risk management.
  • Establishing dynamic data and trend analysis models, to identify anomalies that keep on occurring across organisation’s value chain and customer life cycle. and build robust risk management MI and dashboards that can get discussed at the right forums in the organisation to bring senior management oversight.


Prototype: Once we have identified potential solutions, we can create prototypes to test and refine the ideas.

For example, 

  • we might create a prototype of a new training program for employees or a new fraud detection system. These prototypes could be tested with a small group of users to gather feedback and refine the ideas further.
  • Build robust risk management MI and dashboards that can get discussed at the right forums in the organisation to bring senior management oversight and seek feedback from these committees and further refine the analytical models and reporting tools.


Test: The final step is to test the prototypes with a larger group of users and gather data on how they interact with the solutions. This could involve conducting pilot programs, surveys, or focus groups to gather feedback on the effectiveness of the solutions. Based on the feedback, we can refine the solutions further or go back to the ideation phase to generate new ideas.

By using design thinking to manage operational risk in a bank, we can develop more effective and user-centered solutions that reduce the likelihood of operational risks and mitigate their impact on our customers/clients, bank, and its stakeholders.

Given we discussed how design thinking can be used in designing effective risk management capabilities/frameworks, let’s look at some examples of Data driven Risk management capabilities organisations have been experimenting and investing in the past decade.

As we all now know that
  • Data-driven risk management involves the use of data analytics and quantitative techniques to identify, measure, monitor, and mitigate risks in various domains. With the below examples I would like to bring them to life.
  • Financial Risk Management: Financial institutions use data-driven risk management to mitigate risks associated with their investment portfolios. They use models to analyse credit risk, market risk, and liquidity risk. For example, they use historical data to predict the probability of default on loans and investments. Based on which firms build their ‘collection strategy and also set aside provisions to address this possible default by form of ‘Laon impairment charges – LICs’
  • Operational Risk Management: Data-driven risk management is also used to mitigate operational risks such as equipment failure, employee errors, and process inefficiencies. Organizations can use data to identify the root cause of operational risks and to implement corrective actions.
  • Cybersecurity Risk Management: Organizations use data-driven risk management to identify vulnerabilities and potential threats to their information systems. They use machine learning algorithms and statistical analysis to detect anomalies in network traffic and to identify potential threats. They also use data to assess the effectiveness of their security controls.
  • Supply Chain Risk Management: Supply chain risk management involves identifying and mitigating risks associated with suppliers, materials, and logistics. Data analytics is used to identify potential disruptions in the supply chain, such as natural disasters or transportation delays. It also helps to identify suppliers that may be at a higher risk of bankruptcy or fraud.
  • Healthcare Risk Management: Healthcare providers use data-driven risk management to mitigate risks associated with patient safety, medical errors, and malpractice. They use data to identify patterns and trends in patient data, such as adverse drug reactions or medical errors. They also use data to assess the effectiveness of their patient safety protocols and to identify areas for improvement.
Return on Investment : Design thinking and Data led risk management, as a value creator, rather than just a cost!
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“Price is what you pay. Value is what you get.” ― Warren Buffett

Overall, data-driven risk management helps organizations to make more informed decisions and to reduce the likelihood of negative outcomes. It allows organizations to identify potential risks before they become problems and to take proactive steps to mitigate those risks.

Furthermore Investing in design thinking on collaboration with data led decision making can provide numerous benefits to businesses and organizations. Some of these benefits are worth focusing on.

  •  Customer-Centric Approach: Design thinking is a human-centered approach to problem-solving, which focuses on understanding the needs, wants, and behaviors of customers. By using design thinking, businesses can create not only products, services, and experiences but also customer centric controls that truly address inherent risks any business will experience while meeting the needs of their customers.
  •  Innovation and Creativity: Design thinking encourages innovative and creative thinking, which can help businesses develop new and unique solutions to problems. While data analytics will further support those who lead innovation to ‘test and learn’ fast and address ‘gaps’ in their ideas in a more dynamic manner. This can lead to the development of strong risk governance and oversight which will give businesses a competitive edge.
  • Improved Collaboration: Design thinking encourages collaboration and teamwork, bringing together people with different perspectives and skills to work towards a common goal. This can lead to better communication, idea sharing, and problem-solving, which can ultimately lead to better outcomes to customers and the firm. Furthermore data analytics based decisioning and gap finding methods with enhance the collaboration between the three lines of defence (P&L owners, Compliance and Audit) and improve effective decision making.
  • Dynamic transformation: By using design thinking, businesses can develop and test prototypes quickly and efficiently. This can help businesses bring products and services to market faster, improve operational transparency and better oversight to the regulators on firm’s readiness to address operational failures.
  • Cost Savings: By identifying and addressing customer needs early in the design process, businesses can avoid costly mistakes, reputational damages, penalties, and rework later on. This can help businesses save time and money, and build strong ‘Trust’ while also improving the overall quality of their products and services.


Overall, investing in design thinking and data led risk management will enable businesses to develop more innovative, customer-centric solutions, improve collaboration and teamwork, and ultimately drive business success.

For these processes and methodologies to bring the best value encourage speak-up culture and learning mindset within your organizations!

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