Financial Services
Data-centric scenario planning can make financial institutions more robust against systemic issues, via comprehensive stress testing and risk prediction based on synthetic projections of their customer transaction data.
Learn more about the use of synthetic populations for the generation of tax collection scenarios.
The importance of stress testing and scenario planning
Recent banking crisis shows the importance of assessing risk in the financial industry, conducting stress tests, and building sound response strategies. Processes to quantify the level of exposure to systemic macroeconomic events and potential impact on business viability, reputation, or compliance, are paramount to deploy the appropriate corrective action plans.
Financial organizations should perform comprehensive stress testing campaigns over their business operations and plans across a range of scenarios representing macroeconomic, competitive, and regulatory environments. Scenarios can be modeled according to specific environmental changes such as:
Interest rates
Liquidity
Corporate and household debt
Economic growth and recessions
Local or global disasters
Changes in tax code
Results of stress tests illuminate financial risks and their severity. Scenario planning gives decision-makers the right visibility over potential outcomes and courses of action, which can then be communicated across operational units in the organization to evaluate underlying causes for risk factors and elaborate strategic plans to mitigate them.
Synthetic data as a pillar of financial scenario planning
Financial scenario planning is useful to set strategic directions in good times, but it is even more critical to prepare for crisis situations. Many crises, such as bank collapses and systemic issues, are difficult to predict due to the complexity of modeling them.
Synthetic population datasets can generate realistic customer records and financial transactions at large scale without compromising personal privacy
Synthetic datasets can help organizations prepare for unknown or rare scenarios. The use of synthetic training datasets to improve AI model development is already in use in the areas of automated training and testing of corner cases in applications such as fraud detection. Learn more about our automated testing solutions.
However, synthetic population datasets have an even larger potential to generate customer records and financial transactions at large scale and projected into potential scenarios, realistically and without compromising personal privacy of customer data. This accurate, flexible financial scenario planning is a formidable weapon for organizations to predict and prepare for potential outcomes.
Some use cases of synthetic data-centric scenario planning and decision-making for the financial industry are:
Customer risk assessment and lending automation
This can apply to individual customers or to customer groups, defined by demographics or socioeconomic attributes, with identified spending and borrowing patterns, and predict events such as mortgage defaults across the customer base.
Customer journey
Personalized offering in financial and insurance products can promote and facilitate interactions based on known customer preferences, which change according to age, socioeconomic changes, and environmental factors of the economy.
Policy evolution
The capability to forecast scenarios of changes in fiscal, economic, and social policy and their impact on the customer base behavior are critical to project business estimations into foreseeable futures.
Revenue Protection and Fraud Detection
Identifying abnormal transactions and behaviors is critical to focus financial crime analysis more effectively into potentially fraudulent events and engineered identities.
Predictive analytics
Creating what-if scenarios such as disasters, financial crisis, recessions and market volatility to create views into the future.
By enriching their existing customer transaction data, financial institutions can enable scenario planning capabilities using realistic, privacy-compliant synthetic customer datasets. This approach strengthens their portfolio risk optimization, forecast financial ratios across scenarios, and enhance value of financial products offered to customers.
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