Home Artificial Intelligence SAP and DataRobot: Elevating Bill Processing with Anomaly Detection and Generative AI

SAP and DataRobot: Elevating Bill Processing with Anomaly Detection and Generative AI

SAP and DataRobot: Elevating Bill Processing with Anomaly Detection and Generative AI


SAP and DataRobot are taking their partnership to new heights by strengthening their collaboration by the mixing of predictive and generative AI capabilities. We now have developed a cutting-edge partnership that may empower clients to generate worth with AI by seamlessly connecting core SAP BTP with DataRobot AI capabilities.  

For example, let’s discover how organizations can harness the facility of predictive and generative AI to streamline bill processing providing a quicker, extra correct and cost-effective different to handbook overview and validation.

The Enterprise Downside

Proper now corporations of all sizes grapple with a standard problem:  the relentless inflow of invoices.  The substantial quantity of economic documentation will be overwhelming, typically necessitating a military of staff devoted to handbook overview and validation.  Nevertheless this method shouldn’t be solely time-consuming and expensive, but in addition vulnerable to human error, making it a fragile hyperlink within the monetary chain.  

Harnessing the potential of AI is extra vital than ever earlier than.  Companies can make use of predictive AI fashions to be taught from historic bill information, acknowledge patterns, and mechanically flag potential anomalies in real-time.  This not solely accelerates the validation course of but in addition considerably reduces the margin of error, stopping pricey errors. Moreover, the mixing of generative AI permits for the concise summarization of detected anomalies, enhancing communication and making it simpler for groups to take swift and knowledgeable actions.

SAP and DataRobot Built-in AI Resolution

This AI utility enhances bill processing by a mix of a predictive and generative AI to determine irregularities amongst invoices and to speak the problems across the invoices.

  • Leverage Predictive AI mannequin for anomaly detection.
    • Enterprise perspective: Anomaly detection might help determine irregularities, similar to incorrect quantities, lacking data or uncommon patterns, earlier than processing funds.
    • Implementation: Prepare the mannequin utilizing historic bill information to acknowledge patterns and typical bill traits.  When processing new invoices, the AI mannequin can flag potential anomalies for overview, lowering the chance of errors and fraud.
  • Generative AI Summarization:
    • Enterprise perspective: After figuring out anomalies, you will need to talk the problems to the related staff members.  Conventional reporting strategies could also be wordy and time-consuming.  Generative AI might help interpret and summarize the detected anomalies in a concise and human-readable format.
    • Implementation: Leverage a LLM to generate an explanatory abstract of the detected anomalies.  The AI mannequin can extract key data from the anomaly detection outcomes and supply a transparent and structured narrative that summarizes the detected anomalies and the explanations to be thought-about anomalies, making it simpler for analysts and managers to know the problems. 

Structure and Implementation Overview

To realize these targets, our platforms make use of varied integration factors, as illustrated within the structure graph under:

Graph 1. Architecture overview for the SAP - DataRobot Integrated Solution
Graph 1. Structure overview for the SAP – DataRobot Built-in Resolution
1. Knowledge preparation and ingestion 

Bill information is ready and parsed in SAP Datasphere / HANA Cloud.  DataRobot accesses and ingest this information from HANA Cloud by a JDBC connector.

Graph 2. DataRobot access to create a JDBC connector with SAP HANA.
Graph 2. DataRobot entry to create a JDBC connector with SAP HANA.
2. Characteristic engineering and predictive mannequin coaching

DataRobot  engineers options and conducts experiments with the bill information set, permitting you to coach anomaly detection fashions that excel at recognizing invoices with irregular or irregular data.  The method you select will be tailor-made to your particular information state of affairs—whether or not you have got labeled information or not.  You may have choices to handle this problem successfully, both with a supervised or an unsupervised method.

On this case, we utilized historic information that had been categorized as anomalies and non-anomalies.  After information ingestion, DataRobot runs an in depth information exploratory evaluation, identifies any information high quality points, and mechanically generates new options and related function lists.   With that prepared, we have been in a position to conduct a complete evaluation by 64 distinct experiments in a brief time frame.  In consequence, we have been in a position to pinpoint the top-performing mannequin on the forefront of the leaderboard.  This method allowed us to pick out the simplest predictive mannequin for the duty at hand.  

Graph 3. DataRobot Leaderboard highlighting the best performing model.
Graph 3. DataRobot Leaderboard highlighting the perfect performing mannequin.

Inside every of those experiments, you have got the chance to completely assess and gauge their efficiency.  This evaluation supplies precious insights into how every predictive mannequin leverages the options inside your bill to make correct predictions.  To facilitate this course of, you have got entry to an array of instruments, together with raise charts, ROC curve, and SHAP prediction explanations, which estimate how a lot every function contributes to a given prediction. These insights supply an intuitive means to realize a deeper understanding of the mannequin’s habits and their affect of the bill information, guaranteeing you make well-informed selections.

Graph 4. This Lift Chart depicts how well the model segments the target population and how capable it is to predict the target, letting you visualize the model’s effectiveness.
Graph 4. This Raise Chart depicts how effectively the mannequin segments the goal inhabitants and the way succesful it’s to foretell the goal, letting you visualize the mannequin’s effectiveness.
Graph 5. SHAP Prediction Explanations estimate how much a feature contributes to a given prediction, reported as its difference from the average. In this example how the delivery Date, shipping and gross amount had an impact.
Graph 5. SHAP Prediction Explanations estimate how a lot a function contributes to a given prediction, reported as its distinction from the common. On this instance how the supply Date, delivery and gross quantity had an impression.
3. Mannequin deployment

As soon as we determine the optimum predictive mannequin, we transfer ahead to transition the answer into manufacturing.  This part seamlessly merges our predictive and generative AI method by orchestrating the deployment of an unstructured mannequin inside DataRobot.  This deployment harmonizes the predictive AI mannequin for anomaly detection with a Massive Language Mannequin (LLM), which excels in producing textual content to speak the predictive insights.  Alternatively, you have got the flexibleness to deploy predictive AI fashions straight inside SAP AI Core, providing an extra route for operationalizing your resolution.

The LLM summarizes the rationales linked to every prediction, making it readily digestible on your monetary evaluation wants. This versatile deployment technique ensures that the insights generated are accessible and actionable in a way that fits your distinctive enterprise necessities. 

Two easy python information simply orchestrate this integration by easy features and hooks that can be executed every time an bill requires a prediction and its consecutive evaluation.  The primary file named helper.py, has the credentials to attach with GPT 3.5 by Azure and accommodates the immediate to summarize the reasons and insights derived from the predictive mannequin.  The second file, named customized.py, simply orchestrates the entire predictive and generative pipeline by a couple of easy hooks.   You will discover an instance of the best way to assemble customized python information for unstructured fashions in our github repository.  

You may have the potential to check and validate this unstructured mannequin prior its deployment, assuring that it constantly produces the supposed outcomes, freed from any operational hitches.  

Graph 6. Validation of the unstructured model before deployment.
Graph 6. Validation of the unstructured mannequin earlier than deployment.
4. Enterprise Software

As soon as the deployment is formally in manufacturing, an accessible API endpoint turns into your bridge to attach with the deployment, seamlessly producing the exact outcomes you search in SAP Construct. 

Graph 7. SAP Build Workflow that includes a module to connect with the deployment of DataRobot via API.
Graph 7. SAP Construct Workflow that features a module to attach with the deployment of DataRobot by way of API.

Subsequent, we craft a enterprise utility for bill anomaly detection inside SAP Construct.  This utility retrieves the predictive and generative output by way of API integration and presents a user-friendly interface.  It presents the leads to a sensible and intuitive method, guaranteeing that monetary analysts can effortlessly add invoices in PDF format, simplifying their workflow and enhancing the general consumer expertise.  

Graph 8. SAP Build Workflow for the invoice approval business application.
Graph 8. SAP Construct Workflow for the bill approval enterprise utility.
Graph 9 - Final output generated in the business application for financial analysts to approve or reject an invoice based on the anomaly prediction and the corresponding LLM summary.
Graph 9. Closing output generated within the enterprise utility for monetary analysts to approve or reject an bill based mostly on the anomaly prediction and the corresponding LLM abstract.
5. Manufacturing Monitoring

DataRobot maintains an oversight over the generative AI pipeline by the utilization of customized efficiency metrics and predictive fashions.  This rigorous monitoring course of ensures the continual reliability and effectivity of our resolution, providing you a seamlessly reliable expertise.   

Graph 10. DataRobot deployment containing the predictive and generative pipeline properly monitored over time with relevant custom metrics.
Graph 10. DataRobot deployment containing the predictive and generative pipeline correctly monitored over time with related customized metrics.


In abstract, the partnership between SAP and DataRobot continues to permit organizations to shortly drive worth from their AI investments, and now much more by leveraging generative AI.  Predictive anomaly detection and generative AI can rework the challenges and dangers related to bill processing.  Effectivity and accuracy soar, whereas communication turns into clearer and extra streamlined.  Companies can now modernize their operations, save time and scale back errors.  It’s time to unlock the potential of this transformative expertise and take your operations to the following stage. 

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Concerning the writer

Belén Sánchez Hidalgo
Belén Sánchez Hidalgo

Senior Knowledge Scientist, Workforce Lead and WaiCAMP Lead, DataRobot

Belén works on accelerating AI adoption in enterprises in the US and in Latin America. She has contributed to the design and growth of AI options within the retail, schooling, and healthcare industries. She is a pacesetter of WaiCAMP by DataRobot College, an initiative that contributes to the discount of the AI Business gender hole in Latin America by pragmatic schooling on AI. She was additionally a part of the AI for Good: Powered by DataRobot program, which companions with non-profit organizations to make use of information to create sustainable and lasting impacts.

Meet Belén Sánchez Hidalgo



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