The evolving role of finance in pharma R&D – an analytics & AI-driven new paradigm
Finance in pharmaceutical R&D is on the cusp of disruption. With a marked increase in decentralized clinical trials, an uptick in deals & co-development, increasing prevalence of novel therapeutics such as cell and gene therapies, and more outsourcing than ever before, pressure is on finance functions to understand the impact on budgets, contracting, and cost recognition. There is also downward pressure for R&D finance to do more with less, streamline operations, and increase accuracy of projections while keeping up with advancements to predictive analytics & artificial intelligence.
The future of R&D Finance needs to be agile, providing relevant insights while implementing leading technology solutions and embracing digital upskilling. The next few years will feature significant productivity improvements, reduced cycle times to perform key financial activities, and improved accuracy, scalability & reliability of financial forecasts and statements. Is your finance function ready to adapt?
Imagine a world where Finance can:
Accurately project costs within 2% of portfolio targets by:
- Harnessing predictive insights to determine the direct costs to operationalize the portfolio, from discovery through commercialization
- Facilitating business development deals and reducing cycle times through the use of industry and benchmarking data, providing live and trusted financial insights
- Anticipating the burn rate/consumption of contracts throughout study execution to more effectively recognize costs
Manage cyclical financial transactions & reporting in 75% less time by:
- Eliminating the need to manually integrate & reconcile data, allowing the books to be closed within 3 days
- Spending 90%+ of their time focused on operational strategies and financial risk adjustments, rather than processing invoices and back office functions
Reduce risk and minimize exposure with limited manual intervention by:
- Automating key controls to pinpoint the projects, studies, and functions that are at the greatest risk for over/under spend
- Accruing for work performed based upon portfolio-standard consumption models
With the right infrastructure, data strategy, people & technology, all of this is achievable.
- Accurately project costs
- Manage cyclical financial transactions & reporting
- Reduce risk and minimize exposure
Accurately project costs
Accurately project costs within 2% of portfolio targets
Traditionally, cost estimation for Pharma/Life Sciences R&D is highly manual & subjective, requiring ad hoc inputs from subject matter experts and producing outputs that have significant margin for error. Many organizations struggle to determine when costs will be incurred over the course of an overall target product, indication or study - causing finance to react, rather than to predict likely outcomes.
This paradigm is quickly evolving, as organizations begin to harness the power of big data and analytics to mine historical & industry benchmark data to better inform cost estimation. Historically, organizations estimate costs through a ‘rule of thumb’ approach, requiring deep therapeutic area subject matter expertise to continuously maintain relevance of forecasting models. Finance functions are pivoting to a data-driven forecasting approach, utilizing learning models to promote a high level of confidence in financial forecasts for both project teams and functional leaders.
R&D Finance has the opportunity to strategically advise on the impact of the schedule of events to overall study spend, driving a more effective development strategy and operationally efficient protocol. Combining real world evidence and refined visit projections (cost of procedures, amendments, and endpoints), R&D finance can support the cost / benefit analysis of adding additional components to the study to support a submission.
Want to make this a reality? The foundational components to bring this vision to life are data standardization and compliance. With a consistent data format, and well structured financial data, machine learning models can be deployed & trained to provide greater accuracy than ever before, with a small level of effort.
Manage cyclical financial transactions & reporting
Manage cyclical financial transactions & reporting in 75% less time
Finance functions spend an inordinate amount of time reconciling data, processing actuals, and booking journal entries on a monthly basis, oftentimes resulting in close cycles that take more than 10 days to complete. While these processes may be a ‘necessary evil’, there is a significant opportunity to maximize productivity and drastically reduce the amount of manual processing required.
Machine learning and intelligent automation can help. Machine learning can be deployed to pick up on historical mapping trends for invoices and reduce the number of line items that need to be remapped to the correct activities and cost accounts. Invoices can be easily categorized and an intuitive user interface can be used to understand the nature of the expenses. As technology continues to advance, it will free up time for finance functions to act strategically and provide greater visibility to project & study teams, while embedding confidence in accrual calculations and financial reporting.
Reduce risk and minimize exposure
Reduce risk and minimize exposure with limited manual intervention
The concept of intelligent process automation (IPA) combines artificial intelligence and automation modalities that can drive productivity and mitigate financial risk. With IPA, project and study heat maps can be created which can provide visibility into areas that require intervention in terms of additional review and updates. Imagine having a tool that scans through your portfolio, intelligently detects the projects and costs that need to be addressed and places them in a queue, based on priority, for your Finance teams to address.
The solution employs advanced analytics to flag key indicators in the portfolio and leverage Machine Learning to identify the costs that have introduced the most risk in the past. You can define the factors that matter the most to your organization (large forecast variance, dramatic timeline shifts, variability in accrual bookings, for example) or you can let the analytical model determine the factors that drive the most volatility in your portfolio. With this solution your projects and studies that actually need forecast refinements, deep dives, and additional dialogue can be prioritized, while the majority of the portfolio can continue to run as business-as-usual. All of this is supported by robust financial controls that provide the level of transparency and auditability that is needed to meet statutory requirements.
Adoption of intelligent process automation can tremendously help cut down extra time required to monitor and address volatile swings in your portfolio driving financial efficiency and productivity, while mitigating audit risk.
Preparing for the future of R&D Finance
The time is now to start building more advanced and efficient R&D Finance functions enabled by leading capabilities, leveraging advanced analytics, artificial intelligence and process automation. These are no longer buzz-words, but are tangible capabilities being enabled today by industry peers. R&D Finance has the opportunity to act as a trusted advisor to R&D leaders, rather than being regarded as a supporting organization. By harnessing intelligent automation and predictive analytics, R&D Finance can drastically improve productivity and drive enhancements to the accuracy and efficiency of financial forecasts.
While this shift will not happen overnight, R&D Finance organizations need to make steady steps to modernize data architecture, optimize integration with external partners, and advance planning information to employ these capabilities. The ROI from investment in these capabilities now will pay dividends for R&D Finance organizations of the future, as well as the broader R&D organization at large. After all, the ‘R&D Finance Reimagined’ vision is closer to reality than you may think.
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