Many companies, especially in the COVID-19 era, struggle to reduce their spending. You have to spend money to make money, right? But the most successful and thriving companies are the ones who have found the best way to optimize their spending.
ElectrifAi clients are of the latter that have reduced costs, increased revenue, and decreased risk by optimizing their spend and vendor management. How have they achieved this, you ask? One of the ways are through the procurement tools, SpendAi and ContractAi.
SpendAi and ContractAi use practical artificial intelligence to make sense of data residing in multiple systems across an enterprise for the purpose of strategic perspectives on spend and the supply base. But what really makes it stand out from similar tools are the ElectrifAi machine learning models that make up the product. ElectrifAi’s seasoned industry leadership and data scientists with deep domain expertise really understand the pain points procurement professionals face.
Three machine learning models make up SpendAi:
- Company Name Standardization
Remove the need for manual data cleansing with our “Company Name” standardization algorithm.
- Vendor Name Grouping
Identify which companies are subsidiaries of other companies.
- Transaction Classification
Every procurement transaction is classified into a standard taxonomy.
Twenty Contract Clause Extraction models make up ContractAi:
Contract Clause Extraction Models
- Leveraging training data from thousands of contracts to properly identify and extract pertinent information, such as Assignment, Limitation of Liability, Termination for Cause, and Governing Law, this algorithm streamlines the review of contracts. The model utilizes semantic extraction to identify clauses, not a keyword search.
- Enterprise-wide contracts throughout the entire organization are automatically and accurately classified by clause, category and more to empower you with the insights needed to assume total control over your contracts.
To classify spend, you have to properly understand who your vendors are. Vendors may be classified in many different ways (i.e. IBM could be labeled as both IBM and International Business Machine) and that causes many problems. If you are classifying 10 or 20 times more vendors than you need to, you never get to classify most of them because you don’t really know what they do for you. But once the vendors are cleaned up, it is much easier to organize them based on what the company does (i.e. Johnson & Johnson falls under Medical).
With ElectrifAi’s vast library of machine learning models, there are also complementary models available that are very useful for procurement professionals. What do each of these models do?
- Recommendation Engine
Evaluate spend passion for purchase categories.
- Supplier Base Optimization
Enhance view of suppliers and design Supplier Management Strategy by identifying ideal suppliers based on supplier behavior across various business functions outside procurement, such as financial performance and credit risk data.
- Pricing Optimization
Using a three-pronged approach to optimize prices and monetize unused pricing power, the Continuous Price Recommender identifies inelastic SKUs and stores to grow margins with price increases.
- Demand Forecasting
Predict the popularity of a certain product or service to evaluate when and how much to purchase.
RPA (Robotic Process Automation) companies are a great example for those who would benefit from using these models, such as cleaning up their data before it even gets into the procurement system. ElectrifAi machine learning models easily integrate into platforms like common RPA companies such as UiPath, Automation Anywhere, Blue Prism, etc.
An example of how RPAs work well with ElectrifAi’s machine learning models is the Credit Card Transaction Fraud model. The RPA is used to move data between different systems when the credit cards are processing. The machine learning is integrated directly into those transactions as they are occurring. As the data is moving from one system to the other, the machine searches for fraudulent activity.
Another example is in healthcare for patient claim denials or discharge missed charges. As the paperwork is going through any system, RPA can be used for any of the EHRs (Electronic Health Record Systems). As any EHR is used to attach the various systems that a hospital or heath care provider uses, it’s possible to check if a claim will be denied or if there are missed charges. Then that can be used to orchestrate the logic of whether a claim will likely be denied and needs additional handling, such as sending an email to the physician who wrote the claim.
There are many use cases that ElectrifAi’s machine learning models can help to reduce spend, increase revenue, and decrease risks. SpendAi and ContractAi are great tools that have helped many businesses increase the success of their procurement teams. And RPA integrations are very useful to increase the range to which the machine learning can reach.
Contact us to find out how we can help your company’s procurement process with practical artificial intelligence and proven machine learning capabilities.