Modernize your D&A strategies: Temporal demand

Sustain the D&A value chain

The demand for timely data and faster processing has grown exponentially for financial institutions in recent years; however, many organizations struggle to select, establish and operate the appropriate processes and technology to ingest data in real-time and run analytics on-demand. This temporal demand for data ingestion and processing can empower firms with faster extraction of data, reducing manual intervention in reporting, faster deployment of models, better predictability of future outcomes, thereby improving better decision-making process and improving bottom line, if done correctly. When done incorrectly – or inefficiently – this struggle impacts all components of the data and analytics value chain and across all functions of the institution. In this post, KPMG discusses how firms can leverage the right processes and technologies to yield an effective temporal demand that sustains the data and analytics value chain.

Before understanding how to increase value, it is important to recognize the continuous dependency between data and analytics on temporal demand. If a process, for example a company’s financial planning and analysis (FP&A) forecast, aims to provide weekly model results to track progress, then it is essential that the integrated models that make up the FP&A take no longer than a week to run – ideally even less. But that just covers the temporal demand on modeling and reporting. To provide real value, the information going into those models must also be updated at least weekly. Input data that relies on long-run batching might be updated only once a month (or quarter), rendering the FP&A results stale. And these unbalanced dependencies can further spiral. Some data may be reliant on upstream models. And if those models take a few days to complete, then the data must wait, and the FP&A must wait even longer.

The above example takes a simple, yet ubiquitous process across companies, and shows the precision and balance needed to address temporal demand. When we think of highly regulated industries, such as banking, lower temporal demand can be risky as well as costly. For instance, pricing and interest/market rate models can expose banks throughout the income statement and balance sheet.

  • Throughout the banking industry, pricing models have typically relied on market information for interest rates and static credit rating scales. But there are risks and costs to these strategies. Following market trends in rates can cause a bank to lose out in rate declines. And static credit assessments may expose a bank to unbalanced risk concentrations or miss out on underserved borrowers. Advancements in interest rate forecasts and credit risk analysis have allowed banks to turn these missed opportunities into revenue gains, providing actionable information sooner and clearer.
  • In banks, model monitoring is typically performed quarterly or annually, which creates risks under highly stressed and volatile conditions. This risk is further increased for models that need to be run more frequently than they are monitored, such as liquidity risk. From tracking model degradation to making routine updates, model changes during these conditions can increase the risk around information quality and decision-making, information and decisions that can make or break a bank. Achieving real-time continuous improvement and deployment (CI/CD) through AI/ML and platform optimization is critical to address increased temporal demand under stress and volatility.

As banks begin adopting more powerful and integrated platforms, particularly in the cloud, KPMG sees a significant shift in temporal demand. And this shift directly drives business value, as banks are able to become better informed, lower their costs and establish strategic visions.

Current State



Future State


Business Process

Current State

Future State

Comprehensive Capital and Analysis Review (CCAR)


As CCAR testing is primarily considered an aspect of regulatory reporting, capital planning and adequacy evaluation is often performed only once a year.

By increasing computing power through cloud migration, CCAR models can be run monthly/quarterly instead of annually, allowing for better intra-year capital planning abilities.

Portfolio Monitoring

Evaluation of portfolio allocation and performance across business units and product lines is often limited to semi-annual/quarterly evaluation due to data availability.

Portfolio performance can be measured at more frequent intervals, providing management with a better understanding of which strategies are working and which require adjustments.

Demand Forecasting

Forecasting cash and product demand on a quarterly basis can lead to increased opportunity costs as capital is not effectively deployed and market demand is not met.

Demand can be evaluated on a more regular basis, allowing for better allocation of capital and resources to meet a fast-changing market landscape.


With a large range of risk factors considered in decisioning models, evaluation on a quarterly/monthly basis excludes the latest data points reflecting current market conditions.

With real-time data integration for decisioning models, underwriters can better evaluate macroeconomic and idiosyncratic risk factors to make better lending decisions.

Interest Rate Risk

While interest rate risk is usually evaluated on a weekly basis, the full range of potential scenarios are not captured due to limitations in computing power for sophisticated models.

Greater capacity to process data can provide treasury teams with a real-time understanding of region-specific interest rate risk across all dependent product lines.

How KPMG can help

KPMG has deep experience with clients in improving temporal demand. We provide holistic approach with financial institutions to achieve gains. Our approach covers evaluating data quality, accessibility and management; cloud and hybrid transformation; improving model run-time and code optimization; integrating the full model life cycle; and evaluating the full scope of people, processes and technologies that drive temporal demand.


Assess Current State People, Processes and Technology


Evalute Opportunites to Improve Temporal Demand


Establish Future State Technology


Coach People and Adjust Processes to Leverage Future State Technology


Converge and Optimize Data & Analytics around Future State


Contact us

David Anderson

David Anderson

Managing Director, Risk & Compliance, KPMG US

+1 309-337-8211
Adam Levy

Adam Levy

Principal, Modeling & Valuation, KPMG US

+1 312-665-2928
Alexander Smith

Alexander Smith

Managing Director, Modeling & Valuation, KPMG US

+1 312 665 2183