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PROCESS AREA
Autonomous Finance
MICROSOLUTIONS
Autonomous Accountant

Autonomous Accountant

AFC Data Healing and Syndication – Are your data health practices self-defeating, or self-healing?

Upstream, downstream – the number of error off ramps across enterprise systems can take a toll on data accuracy – with nothing but time-intensive manual intervention as the cure. Master your data anomalies, detect errors, automate approvals and cross-platform data consistency with the power of ML.

The AIOPS.D Advantage
Standard Practice

Generates Master Data

The Autonomous Accountant dashboard displays the list of all data transactions pulled from the organization’s ERP system to create master data.

Detect Data Anomalies

Common Challenges:
  1. Manual review of transaction data to detect issues/anomalies is time-inefficient
  2. Complex rules for workflow creation drives incorrect classification of transactions
  3. Manual exception handling, including identification of data outliers, rectification process, workflow creation and data validation

Detect Transaction Errors

Common Challenges:
  1. Manual transaction posting and mapping drives data inaccuracy
  2. Inconsistent timing between data availability and transaction posting creates errors across workflow

Approve Journal Entries and ERP Posting

Common Challenges:
  1. Manual, time-intensive review and approval of journal entries
  2. Siloed correction requests outside of workflow leads to upstream correction delays
  3. Manual intervention is required to ensure data mappings are regularly updated

Maintain Data and Provide Feedback

Common Challenges:
  1. Manual reminders across stakeholders to complete updating tasks
  2. Checking the system manually for status updates may lead to missed items
  3. Delayed feedback on transaction data posted to an ERP system
  4. Limited feedback shared with users on account/ transaction creation and posting
  1. ML-based process to creating / updating the attribute data in the master data across all relevant systems
  2. ML-based exception handling enables accurate data sync across systems for consistent master data
  1. ML-based anomaly detection for transaction data including erroneous data and missing data fields
  2. Transactions are validated after they have been posted in a defined scheduled to identify anomalies
  3. ML-based identification of data outliers and workflow generation for rectification
  1. ML-based algorithms identify issues based on error logs to improve data accuracy
  2. Accurate data available immediately after posting, increasing accuracy of data
  1. ML-proposed Journal entries for review based on identified anomalies
  2. Provides ML-driven data correction workflow with an audit trail and visibility into every stage of master data maintenance across systems
  3. Autonomous validation of data mappings for consistency across systems
  1. Real-time feedback on task updates by connecting to all relevant systems via APIs
  2. Self-learning mechanism to automate system updates, with validation across systems
  3. Automated notifications for data posting across systems, including ERP platforms
  4. Intuitive, conversational AI-based self-service dashboard for status transparency

OVERVIEW
DESCRIPTION
AIOPS.D™ Autonomous Accountant leverages ML models to continuously monitor data quality issues and autonomously perform corrections, to reduce the time spent on manually intensive month end financial activities. ​

The solution autonomously compares and cross references data from multiple sources to ensure data quality. It can obtain data from various sources and reconcile it across the systems without any human intervention. ​
STORIES FROM THE AI VAULT

CAPABILITIES

Autonomous detection of data anomalies
ML-driven engagement for master data governance
Real-time feedback
Self-learning mechanism to automate system updates

MICROSERVICES
Autonomous Accountant

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DESCRIPTION
AIOPS.D™ Autonomous Accountant leverages ML models to continuously monitor data quality issues and autonomously perform corrections, to reduce the time spent on manually intensive month end financial activities. ​

The solution autonomously compares and cross references data from multiple sources to ensure data quality. It can obtain data from various sources and reconcile it across the systems without any human intervention. ​
STORIES FROM THE AI VAULT

PROCESS AREA
CAPABILITIES

Autonomous detection of data anomalies
ML-driven engagement for master data governance
Real-time feedback
Self-learning mechanism to automate system updates

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