Predictive Analytics for Today’s Intelligent Finance Management: The Whys and Hows
Kanticha Panyawachira, Senior Consultant, APAC, Wolters Kluwer | CCH Tagetik
As Covid-19 vaccination programs gather momentum around the world, the end of the pandemic appears to be in sight. However, we’re not out of the woods yet. It remains unclear how long it will take for immunisation efforts to bring about herd immunity, which is a prerequisite for lifting coronavirus restrictions. Vaccine production is struggling to keep pace with demand, and distribution drives have been complicated by the emergence of new variants of the virus.
One thing is certain: there will be no return to “business as usual”, even when the pandemic has come to an end. Covid-19 has reshaped the business landscape profoundly and irrevocably. More than ever before, companies need to be able to adapt quickly to changes in consumer behaviour, government policy, and economic conditions. Building their business agility now becomes an imperative, in order to identify potential risks and uncertainties before they arise and deal with them proactively.
CFOs, in particular, need to ensure that they are making well-informed decisions since an organisation’s long-term success depends to a large extent on good financial management. CFOs can no longer rely on traditional methods of financial forecasting as these tend to be time-consuming, labour-intensive, and prone to human error. Rather, they will need to leverage modern technologies such as artificial intelligence (AI) and machine learning (ML) to automate the collection and processing of data so that they can focus on making faster and better-informed decisions.
By doing so, critical processes such as forecasting and scenario-planning are empowered with next-level Predictive Intelligence which will adequately equip the organisation in finding opportunity even in the face of disruption.
Why Smart Finance Leaders are Leveraging Predictive Intelligence
1. To improve cash analysis and revenue forecasting
Predictive analytics can be used to enhance cash flow forecasting. This basically involves analysing historical data and key trends to predict future events affecting cash flow.
2. To accelerate planning cycles and improve decision making
Predictive intelligence can be employed in scenario planning, enabling CFOs to quickly map out multiple possible future scenarios and thereby identify potential opportunities and obstacles and strategise accordingly.
3. To analyse loss drivers and revenue risks
Finance teams can use predictive analytics to examine sales figures, consumer trends, and other types of data to identify potential loss drivers, enabling executives to take corrective actions and thereby maximise revenue.
4. To detect fraudulent behaviour
Organizations are increasingly deploying predictive analytics techniques to detect potential fraud—both internally and externally—in order to take preventative measures.
5. To plan demand
Predictive analytics has proved to be a powerful tool for forecasting consumer demand for particular goods and services. This enables an organisation to develop better customer experiences which in turn maximises profitability.
How to Plan Your Predictive Intelligence Journey
Step 1: Prepare your data
Accumulating and organising good quality data is essential for accurate forecasting. You will need to collect both financial and operational data and use machine learning technology to check the quality of the data.
Step 2: Set up profit and loss forecasting
Machine learning tools can be applied to time series forecasting to accurately predict profits and losses. This step is crucial in developing a good enterprise planning system.
Step 3: Employ contributor analysis
Data validation is key when it comes to predictive intelligence. Simple data entry errors are fairly easy to detect but, when dealing with vast amounts of contributed data, you will need to employ machine learning algorithms to uncover more complex issues like contradictions and conflicts of objectives.
Step 4: Set up a driver-based simulation
Driver-based modelling allows you to make predictions about the financial performance of your organisation by modelling the mathematical relationship between operational drivers and financial outcomes. The use of driver-based simulations can improve management’s capacity for decision making.
How CCH Tagetik Can Help
Leveraging Predictive Intelligence is essential when building an agile organisation that can respond effectively to ever-changing market dynamics. However, laying the groundwork for a predictive intelligence system can be challenging for finance teams given the large amounts of data required and the complex processes involved. This is why it’s important to partner with a reliable solutions provider which has the technical know-how to walk you through the stages of establishing an effective system.
CCH Tagetik can help in this regard. Our Analytic Information Hub is able to process high volumes of granular financial and operational data at speed, enabling finance teams to generate insights from the data to drive in-depth planning, profitability analysis, and more.
CCH Tagetik is able to support contributor analysis by using the latest machine learning tools to automatically detect outliers and inconsistencies in contributor data. We are also able to set up driver-based simulations to take corporate performance management to the next level.
Finally, we have developed a new product which further strengthens our ability to support finance teams in the predictive intelligence journey.
Please visit our website if you would like to know more about the solutions we provide. You may also find our free ebook "8 questions finance needs answered before leveraging Predictive Analytics" helpful.