Poor spend management can spell disaster for businesses, particularly during economic turbulence. It's during such challenging periods that the importance of efficient spend management becomes glaringly evident.
One notable advancement in this field is the growing use of machine learning-based data analytics to optimize spend and invoice control. Also, AI technologies can also be leveraged to help address this problem.
Tech Mahindra BPS leaders Mr. Kishore Malethula, Group Practice Head, Mr. Aditya Jha, Principal Consultant, and Mr. Abhay Tiku, Principal Consultant got into an insightful discussion about industry trends and how Tech Mahindra is catering to them with its AI/ML analytics solution for spend management along with moderator Mr. Aadithya Subramanian, Associate Business Consultant.
Value Addition with Spend Data Analytics
Data can help tremendously in decision-making, and sometimes just to validate decisions for the correctness. But the primary challenge, irrespective of the industry or vertical, is that spends are spread globally with no certain way to ensure ROI. A couple of examples would be the cost of deploying equipment and procuring costs. Information like this structured over processes and verticals may be of no use. With AI/ML-based analytics, businesses can make sense of this data and drive real value, including cost and revenue optimization.
On the same lines, Abhay adds, “There are different categories of spends in different industries, but spend management problems manifest themselves in a similar fashion. With complete data solutions in addition to frameworks, organizations can be more aware about spends at granular levels.”
Data Analytics for Invoice Control – When an invoice comes in, it is checked for approval or non-approval. But as all data is valuable, what businesses need is a single platform where all invoices that need to be paid must go, approved or not. This is when invoices act as data. Analyzing this data can help understand patterns of approval or non-approval, enabling machine learning and automation of processes like this.
Spend Management – When an invoice comes in, the right spend needs to go through quickly and any flags must be checked for. But potential errors like duplicates, payment delays, frauds, and others, can often take place. Spend analytics can break down data in line-level items, allowing for inspection and analyses of data at a granular level. With the use of forecasting based on insights, businesses can find and make the most of cost and time-saving opportunities. Operational Challenges in AI/ML-Powered Spend Management
Here are some challenges observed:
Governance and Control Mechanisms: When you try to bring in a new way of making decisions in organizations, proper governance and control mechanisms are critical. The governance models need to be flexible enough to let the processes adapt to the new solution and tight enough to ensure that things are moving in the right direction.
Integration with Existing Systems – Data is always unstructured. Therefore, it takes a lot of learning to ensure that data quality is up to the mark, something which can be influenced by the seamlessness in integrations.
Kishore adds to this, “Machine learning models need vast amounts of data; they learn from data patterns and every bit and piece counts. The integration must be done in a way that the spend management models are exposed to the seasonality and variations in data for it to be precise.”
Use of Non-System Data – The spend management tool must also address the challenge of non-system data to allow businesses to better understand the outcome data. With the access to complete data, AI/ML models can suggest the levels of caution that business managers must exercise when dealing with the outcome data.
Aditya speaks about the use cases of AI/ML-based analytics in spend management and Tech Mahindra’s analytics KPIs, “We offer end-to-end analytics solutions that go beyond invoice management. We understood what was missing in the spend management ecosystem and leveraged the power of AI to create real business value for our clients, including savings on spend management, reduction of revenue leakage, reduction in invoice approval time, and an overall improved operational efficiency.”
Tech Mahindra’s AI/ML-based decision-support system enables a comprehensive control mechanism that streamlines spend management and invoice control processes. We help our clients take an analytics-based approach and automate operations for improved cash visibility and flow, and also ensure insightful decision-making using AI.
To learn more about our decision-support system, watch the recent Tech Talk with TechM leaders here.
About the Author
Mr. Kishore Malethula
Group Practice Head – TechM BPS
Kishore has over 22+ years of work experience, largely into data and analytics. He has been analytics practitioner, developed various analytics platforms / products and worked with global clientele across industries and cultures. He holds a B. Tech degree in Manufacturing Engineering from National Institute of Advanced Manufacturing Technology (NIAMT), Ranchi, and has done his MBA from IIM Calcutta. He enjoys working on innovative projects and have successfully delivered multiple engagements in the AI/ML space. He is passionate about creating learning opportunities and shaping peoples’ careers while delivering business results. He is an industry recognized mentor/coach and an esteemed speaker at various analytical/industry forums. At TechM, he heads the BPS Analytics practice.
