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Introduction: In this blog, we will walk through below listed technology trends that helps to transform and digitize Financial Planning & Analysis (FP&A) function. These trends are likely to bring about a paradigm shift in connecting business functions real-time and predicting future to make faster & pro-active decisions.
o Artificial intelligence & Machine Learning
o Virtual Assistant, Bot and Chatbot
o Blockchain, Blockchain-as-a-Service
o In-memory computing
o Robotic process automation
o Connected & Real-Time Planning, What-If Scenario Analysis
o Predictive Analytics & Visualization
o Cloud Computing
o Software-as-a-Service (SaaS), Finance-as-a-Service(FaaS), FP&A-as-a-Service
o Big Data & Small Data
o Internet of Things (IoT)
o Digital Finance Transformation (DFT)
Background:
In the late 1980’s when Enterprise Resource Planning (ERP)systems were gaining popularity, most implementations were carried out by engineers and system experts. As the projects became complex, customers who had to re-engineer their existing business processes to accommodate the software, were dependent on consultants with functional and industry specific backgrounds. The demand for functional experts in Finance & Accounting, Supply Chain, Marketing/Sales increased multifold which lead to new job roles like Functional Analyst & Business Systems Analyst (BSA). By late 1990’s, capability of ERP system was extended to address Customer Relationship Management (CRM), Supply Chain Management (SCM), Product Lifecycle Management (PLM), Supplier Relationship Management (SRM) & Human Resource Management (HRM). By then, Project Management Office (PMO) started to govern IT project implementation & change management. By early 2000, with the addition of Data Warehouse (DW) &Enterprise Performance Management (EPM), the complexity of systems with voluminous data created additional roles in Systems Integration, Data Warehouse, Archiving& Analytics. By then, Governance, Risk & Compliance (GRC) team controlled the Security & Access provisioning.
With the phenomenal growth of computing power and Internet in the last decade, Cloud, In-Memory & Mobile Computing gained popularity and new business models like XaaS (X-as-a-Service) emerged. Below SaaS applications are gaining popularity and replacing on-premise legacy applications.
o Sales Cloud
o IT Cloud
o Service Cloud
o HR Cloud
o Planning Cloud
o Analytics Cloud
o CPQ Cloud
o Cloud Integration
While Neural Networks & Natural Language Processing (NLP)became today’s buzz words, one can get lost when they deep dive into the ocean of Artificial Intelligence, Machine learning & Big Data. As the market matures, there is a need for functional experts to help in delving deeper into structured and un-structured data within and outside company’s network to decipher relationships in the data and processes. This paper tries to make a simple representation of how machines can help business, especially those who support Financial Planning & Analysis (FP&A) function, to create an efficient work life.
Storyboard:
As an analyst, I would love to employ a team of Bots and train them to help me with aggregating and verifying data (historical analysis), so I can focus on analysis, generate actionable insights for real-time decision making. I will have more time for strategic planning, knowledge sharing, business partnership and predictive analysis to drive business results. Within many enterprises, finance functions can spend around 70% of their effort on data collation, enrichment and validation. This elongates the financial close and time to report.
LokiBots is a cloud-based SaaS offering on GCP platform which has a flexible charging model using Pay-As-You-Go pricing based on number of Bots and its usage. Once subscribed, my access to various applications with in my company’s corporate firewall are cloned and provisioned to all my Bots. I allocate time to provide knowledge transfer (KT) to these Bots and monitor/evaluate their performance regularly (Supervised Learning).
My first bot, using Pattern Recognition, helps in analyzing past issues/incidents raised by FP&A team in last 5 years, sends me alerts before month-end, qtr-end by listing top 10 expected issues and their priorities. It also develops an action plan to prevent those issues from recurring in future and recommends what actions to betaken before every close cycle.
The second bot, studies my activity trend and identifies list of reports/templates I use frequently, opens them to refresh in-memory. So, when I login to an application, I can directly consume the data without having to wait for screen refreshes and data sync-ups. It also scans millions of finance data points and identify statistical patterns, driver correlations and reports the insights to me for further analysis. It also runs the Opex allocations at regular intervals which usually take 2 hours to complete during quarter-end. It provides insights on what % of operating expenditure is for Sales & Marketing compared to Engineering, R&D. These % numbers are compared internally for last five years, compared with competitor’s trend and industry benchmarks.
In the meantime, my third bot is logging into various applications like ERP, EDW, Planning applications, Dashboarding tools etc., to check if the scheduled jobs are completed on-time, read through the run logs and error logs, check the pre-defined reports based on rule-based data validation and trigger alerts. It also responds to self-service requests via Chatbot from my business partner who would like to know total revenue for APAC region compared with EMEA. It makes a department owner happy by answering his request by emailing a list of Top 10 employees based on travel spend.
My fourth bot uses algorithms to deep dive into the financial numbers to provide me
o Opex assumptions
o New Hire Recruiting Fee
o Relocation Fee
o Health Insurance, etc.,
o Sales assumptions & Growth rate,
o Commissions assumptions
o Renewal Commission Rate
o Service Commission etc.,
o Revenue Driver assumptions
o Upsell Rate
o Renewal Churn rate
o BESP
o Average Contract Length etc.,
o Currency Exchange rate prediction
It also challenges the Sales Forecast received from Sales teams and helps in improving the forecast numbers by starting the machine learning journey using visual and transparent models such as regression and decision trees.
Fifth bot who works part-time, handles administrative tasks like tracking time sheets, alerting meeting conflicts and suggesting alternatives, watch the leave plans of colleagues to evaluate impact to my deliverables and so on. It also collects data (using Internet of Things - IoT) for tracking my reportees attendance, location, equipment, vehicle check-in, VPN logins & Privilege Leaves. Using Artificial Intelligence & Machine Learning to measure employee engagement, we can trigger surveys during key events (marriage, parental leave, project go-live, working weekends, sabbaticals, long sick leaves) and get valuable insights to predict attrition/turnover and recommend ways to avoid it.
o The Analyst performs a sequence of actions which is recorded by the BOT.
o While recording, the BOT’s will attempt to analyze or interpret what the user did. So, while mimicking the same action, even when the screen is changed, the BOT’s will be able to identify the object (App or report or dashboard) it has to work on.
o The BOT will have ability to continuously learn new skills and adapt the existing skills to new contexts.
o The algorithms (Deep Q-network - DQN)used by the BOT will be flexible and versatile.
o The BOT will be able to differentiate the TASKS performed by user (Opening a web page, opening a report, executing a job etc.,)
o The BOT will be able to segregate META DATA, MASTER DATA & TRANSACTION DATA
o The BOT will be able to treat MASTERDATA as a variable, so different values can be passed during each iteration.
While my Bots continue to deliver and provide insights on historical data using Big Data, I can deep dive into insights (Small Data) generated by my Bots and provide forward looking insights. I can focus on building an architecture for connected planning platform where I can bring together operational and financial data. My goal is to create a single planning platform to support
Finance planning
o Sales Capacity
o Cohort Analysis
o HC Forecast
o Sales Forecast
o Sales Budget
o Spend Forecast
Sales Performance Management
o Incentive and Commissions Management – ICM
o Opportunity and Customer Management
o Territory& Quota Management - T&Q
Any updates to territory assignments and quotas will sync up with ICM application real-time. Based on the goals and constraints, Bot can compare hundreds of what-if scenarios and iterations for commissions in ICM application, then share the top 3 options with me to analyze and freeze the best available option. This in-turn updates the commissions forecast in FP&A application real-time. A single dashboard using latest visualization techniques can display real-time Opex & Capex expenses to department owners and help to manage exceptions.
Conclusion:
While Bots help with better data insight, automate mundane tasks and data validations, Analysts can own decision making, be accountable for decisions taken, take charge of compliance and spend more time in understanding the business. They can leverage latest technology trends to build credibility and become trusted business partners. During Performance Evaluation of Bots, one of the expected KRA’s would be to focus on customer success rather than challenging and criticizing actions and decisions!
