AI can improve data collection
Anders Liu-Lindberg
Leading advisor to senior Finance and FP&A leaders on creating impact through business partnering | Interim | VP Finance | Business Finance
Teaser: Gathering data for FP&A using artificial intelligence takes good management and understanding, but as it learns, its accuracy continuously improves.
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Collecting financial data from non-financial people has its challenges, but accounting systems are learning to make life easier. Where once small firm accountants waited in trepidation for the trader’s brown bag of receipts to make up the books, now the same trader is using a simple app to scan their receipts. They are read and interpreted by AI and the details are posted to the right place in the ledger.
This same technology helps enterprise-level input from operational divisions, with machine learning within ERP systems ensuring that transactions are treated consistently and logically. Autocomplete, the AI’s assumption of the most likely next word in the phrase, helps with good annotation. Voice recognition technologies and language prompts allow people to provide the data that’s needed without moving out of their comfort zone.
Applications like ChatGPT can also help with communication. This is a great tool to simplify language, cutting through the jargon so that peers in other functions understand what’s needed and provide the right data for decision-making.
The computer isn’t always right
The FP&A leader still has to manage their AI team member. The computer is not always right, and understanding how AI interprets information is essential in maximizing its effectiveness and avoiding errors.
Autocomplete, like ChatGPT, works on the principle of “what’s the most likely next word”. It draws from the data that it has, and in a closed system like finance, it can be very accurate as it learns it has a limited range of options. Out in the wild, it draws on far more data and takes time to be trained.
Voice recognition is one particular area of AI that is fraught with problems. Research shows that spoken English words that recur in data sets, like date or time, have an error rate under 5%, but AI struggles with non-native speakers, regional accents, and less common languages because there are not enough hours of voice recordings to train the machine effectively.
The names of organizations and people are a particular challenge. As the data sets get smaller, errors rise to the point where it’s really just guesswork. If you’re collecting data using voice systems, manage it with care.
A system that works perfectly at the head office where the AI is trained to recognize local voices will not be as accurate when it’s rolled out to multinational locations. Errors in collection and labeling perpetuate the problem of poor data, and before you know it, you’re making the wrong decisions.
Managing effective AI data collection
Human oversight - good team member management - is important. While AI can be trained to collect data autonomously, human oversight ensures that the data being collected is ethical, unbiased, and complies with relevant regulations. AI can be trained to collect good data in several ways.
Do you know which of your systems already use AI to capture data? How do you ensure that the data collection is effective and accurate? Let us know in the comments.
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Anders Liu-Lindberg?is the co-founder and a partner at?Business Partnering Institute?and the owner of the largest?group dedicated to Finance Business Partnering?on LinkedIn with more than 11,000 members. I have ten years of experience as a business partner at the global transport and logistics company?Maersk. I am the co-author of the book “Create Value as a Finance Business Partner” and a?long-time Finance Blogger?on LinkedIn with 190,000+ followers and 255,000+ subscribers to my blog. I am also an advisory board member at?Born Capital?where I help identify and grow the next big thing in?#CFOTech.?Finally, I'm a member of the board of directors at?PACE - Profitability Analytics Center of Excellence?where I support the development of new analytics frameworks that can improve profitability in companies around the world.
Available | Senior Business Controller | Finance Business Partner | FP&A | Insurance | Banking | Asset Management | Real Estate
1 年I’d say all data collection has its challenges rather than stressing a divide between finance and non finance. Otherwise great point to look at the added value AI can provide.
I lead a forward-thinking, innovative leadership community dedicated to revolutionising the finance function in the digital era. Its members are catalysts for change within the profession, whom we connect & celebrate!
1 年Great shout @anders
Senior Finance Leader | CFO| Ex. Cornell Alumnus |Head of Finance | Digital Transformation | Global Accounting| Shared Services | Process Improvement
1 年Anders, very relevant facts on Data and AI. From my experience, I have come across data being captured to see the Operating time of an equipment vs. Idle time. The anlaysis on these, which were more accurate had been far reaching. Time to plan and required for maintenance, How to price your tenders based on historical operating time for operating rate vs. standard lease per month. The same set of information can pivot and help organizations in many different ways.
Helping Finance Professionals Build Confidence, Ace Interviews & Communicate with Impact | 4+ Years of Empowering Careers Through Expert Coaching | 12 Years as an Accountant Turned English Coach
1 年Great article Anders. Using the tools available is great in getting tasks done faster but as mentioned you have to check and be sure the data provided is accurate :)