Integration with the Oracle Autonomous Database: EPM Monthly June 2026 patch

Oracle Autonomous Database is a fully automated, cloud-native data management service that self-drives, self-secures, and self-repairs.
It eliminates manual database administration—handling automatic patching, scaling, tuning, and backups with no downtime. It serves various workloads, including AI, transactions, and data warehousing.
From EPM Monthly June 2026 patch, Cloud EPM customers can now connect directly to the Oracle Autonomous Database to extract data from it and load the data to the Cloud EPM, eliminating the need to use the agent for this type of connection. This type of integration enables you to extract source data from staging or other applications running on the Oracle Autonomous Database.
Additionally, customers can also use the external table function in the Oracle Autonomous to define a table where the source data is from a file in Object Storage and then extract data from it.
Applies to:
ARCS, FCCS, TRCS, NRCS, Freeform, Enterprise Profitability and Cost Management, Planning, Profitability and Cost Management

Business Benefit:
Prior to this update, in order to extract data from the Oracle Autonomous Database, Cloud EPM customers had to install the EPM Integration Agent and add on a compute instance.
Cloud EPM customers now have a set of easy-to-use, no-code tools to load data stored in the Oracle Autonomous Database.

Thank you!

The Journey of Oracle ARCS: How Automation and AI Rewrote Account Reconciliation

 Account reconciliation has historically been one of the most labor-intensive phases of the corporate financial close cycle. For decades, corporate accounting teams were trapped in a cycle of manual data extraction, disparate spreadsheets, and fragmented audit trails. This reality exposed organizations to significant operational risk, compliance bottlenecks, and delayed financial reporting.

The evolution of enterprise technology has fundamentally altered this paradigm. At the forefront of this shift is Oracle Account Reconciliation Cloud Service (ARCS). Over the past two decades, this solution has transformed from a rigid, infrastructure-heavy on-premises tool into an agile, cloud-native suite, and ultimately into an autonomous, AI-driven framework.

1. The Legacy Era: Hyperion ARM and On-Premises Constraints

Before the maturity of cloud computing, enterprise reconciliation was primarily managed via legacy systems. Oracle’s flagship offering in this space was Hyperion Account Reconciliation Manager (ARM), deployed as an on-premises module within the broader Hyperion Financial Close Management ecosystem.

While Hyperion ARM successfully digitized the foundational workflow of reconciliation—establishing formal paths for preparers and reviewers—it presented clear operational limitations:

• High Infrastructure Overhead: On-premises deployments required substantial capital expenditure for server maintenance, database administration, and iterative, disruptive upgrade cycles.

• Rigid Customization: Creating transaction matching rules or complex data integrations required extensive IT support and specialized script writing, often utilizing Visual Basic (VB) or database procedures.

• Siloed Data Architecture: Data extraction from external General Ledgers (GL) or sub-ledgers relied on batch processing and flat-file transfers, introducing latency into the close window.

2. The Cloud Paradigm Shift (2016–2017)

Recognizing the limitations of localized infrastructure, Oracle officially launched Account Reconciliation Cloud Service (ARCS) as a standalone Software-as-a-Service (SaaS) solution. This pivot to the cloud dismantled the infrastructure barriers that had long hampered corporate accounting teams.

The introduction of ARCS brought two distinct architectural improvements to the market:

1. Reconciliation Compliance (RC): A web-based compliance engine that centralized the monitoring, reporting, and auditing of account reconciliations globally.

2. Transaction Matching (TM): A high-performance engine capable of ingestion and automated matching of high-volume transaction data (e.g., millions of point-of-sale entries against bank statements).

By shifting to a graphical user interface (GUI) and configuration-driven logic, Oracle empowered finance users to build their own matching rules, formats, and workflows independently of corporate IT departments.

3. Consolidation into the Unified EPM Cloud Suite (2019–2021)

As cloud ecosystems matured, the market demanded deeper interoperability. Oracle responded by absorbing ARCS into its comprehensive, unified Oracle Cloud EPM (Enterprise Performance Management) platform.

This consolidation transformed account reconciliation from an isolated monthly chore into a continuous business process. Native integration within the EPM suite allowed ARCS to communicate directly with Oracle Financial Consolidation and Close (FCCS) and broader ERP ledgers. Data flows became automated, giving close managers real-time visibility into reconciliation statuses, variance analysis, and balance sheet integrity directly from centralized corporate dashboards.

4. The AI and Machine Learning Frontier: Achieving the Touchless Close

Today, Oracle ARCS represents the cutting edge of financial technology, utilizing embedded Artificial Intelligence (AI) and Machine Learning (ML) algorithms. The software has transitioned from a system that merely tracks manual accounting activities to an intelligent framework that executes them.

Modern AI capabilities have re-engineered the reconciliation workflow across three primary vectors:

1. Raw Transaction Data

2. Intelligent Auto-Match Engine (Complex many-to-many ML matching)

3. Continuous Anomaly Detection (Real-time variance identification)

4. Risk-Based Auto-Certification (System-generated audit trails)

Intelligent Transaction Auto-Suggest

Traditional rules-based matching engines fail when encountering complex data anomalies—such as timing differences, currency fluctuations, or fragmented vendor descriptions. Historically, these exceptions fell into manual queues, requiring accountants to hunt down matching lines across spreadsheets.

The integrated ML engine analyzes historical matching patterns and human corrective actions over time. When a data anomaly occurs, the system calculates a statistical confidence score and automatically suggestsmatches to the user. This turns a tedious search-and-verify task into a simple, single-click approval process.

Continuous Anomaly and Variance Detection

Rather than waiting for the close cycle to begin to identify ledger discrepancies, ARCS now utilizes background AI algorithms that scan active data pipelines continuously. By establishing a baseline of historical trends and transaction behaviors, the system flags unusual variances, duplicate billings, or suspicious ledger postings in real time. This allows accounting departments to remediate errors prior to the high-pressure period-end close window.

Automated Risk-Based Certifications

A significant portion of a finance team's time during a close cycle is spent verifying low-risk, low-activity, or zero-balance accounts. Oracle ARCS leverages AI to execute automated, risk-based certifications. If an account meets specific low-risk operational thresholds and data validation rules, the AI signs off on the compliance documentation, generates the necessary audit trail, and archives the file. This filters out the operational noise, allowing senior accountants to focus exclusively on complex, high-risk variances.

Looking Forward: The Future of Financial Integrity

The evolutionary path of Oracle ARCS mirrors the broader digital transformation of the corporate finance function. By migrating from the manual constraints of Hyperion on-premises to a cloud-native environment, and ultimately incorporating advanced AI and Machine Learning, Oracle has fundamentally changed how corporations view risk and financial data validation.

The ultimate objective of this journey is the realization of a continuous, touchless financial close. Through persistent AI oversight, automated data harmonization, and self-learning matching logic, Oracle ARCS ensures that financial data integrity is maintained continuously throughout the fiscal period—not just during the first week of the month. For enterprise organizations, this translates to reduced compliance risks, minimized operational costs, and the transformation of the accounting function from historical bookkeepers into strategic, data-driven business partners.

Thank you!

Beyond the Numbers: The Evolution and AI Transformation of Narrative Reporting

For decades, the standard metric of corporate performance was raw data. Balance sheets, cash flow statements, and income disclosures formed the foundation of market trust. However, numbers in isolation rarely tell the whole story.

Today, corporate reporting has shifted toward narrative reporting—the strategic integration of financial data with operational insights, market context, governance, and sustainability goals. It answers not just what happened, but why it happened, what it means, and where the enterprise is heading.

As stakeholders demand machine-readable structures alongside clear human narratives, the discipline faces another critical shift. The following analysis traces the history of this professional discipline and examines how Artificial Intelligence (AI) is transforming it.

The Journey of Narrative Reporting: From Appendix to Center Stage

The evolution of narrative reporting can be categorized into four distinct eras:

1. The Compliance Era (Traditional PDFs)

2. The Stakeholder Era (Integrated ESG Focus)

3 The Digital Architecture Era (Structured Tags (XBRL))

4. The Intelligent Reporting Era (Gen AI & Agentic Workflows)

1. The Compliance Era: Traditional Data Dumping

Historically, narrative reporting was treated as a regulatory checkbox. Annual reports consisted of backward-looking financial tables paired with rigid, boilerplate Director's Reports. The narrative was secondary, heavily curated by legal teams, and provided minimal insight into actual day-to-day business drivers or long-term risk mitigation.

2. The Stakeholder Era: The Push for "Integrated Reporting"

The early 2010s brought a realization: traditional accounting metrics failed to capture intangible value, such as intellectual property, corporate governance, and societal impact. This drove the adoption of Integrated Reporting () and environmental, social, and governance (ESG) frameworks. The "front half" of the annual report grew to equal the "back half," changing reporting into an ongoing narrative of holistic value creation.

3. The Digital Architecture Era: Machine-Readability

Regulators globally began requiring that public narratives be translated into standardized formats. Frameworks like the International Sustainability Standards Board (ISSB) and European Sustainability Reporting Standards (ESRS) converged into a global baseline. Meanwhile, bodies like the UK Financial Reporting Council (FRC) and the SEC mandated structured digital reporting using technologies like Inline XBRL (eXtensible Business Reporting Language), ensuring narratives could be cross-referenced and analyzed programmatically.

4. The Intelligent Reporting Era (Present Day)

The current landscape has shifted from static, multi-authored PDFs to dynamic, interactive reporting ecosystems. Corporate performance is no longer communicated in a single annual document, but through connected data environments that update dynamically and are built for both human review and automated analysis.

The Role of AI in Modern Narrative Reporting

AI is no longer just an experimental tool; it functions as core infrastructure within Enterprise Performance Management (EPM) and disclosure software. Modern enterprise systems (such as Oracle Cloud EPM and Anaplan) embed artificial intelligence directly into the reporting loop, altering how reports are produced and consumed.

AI applications generally fall into two primary areas:

1. The Production Side (Creating the Report)

• Contextual Variance Commentary: Rather than finance teams spending days manually investigating discrepancies, Generative AI monitors financial grids. When a data intersection breaks a specific threshold, GenAI evaluates the point-of-view data, cross-references historical notes, and drafts real-time, natural-language commentary explaining the root cause of the variance.

• Automated Alignment & Consistency Checks: Multi-author reports are prone to internal contradictions. AI models audit entire document packages to ensure a metric cited in the initial executive summary precisely aligns with detailed data tables located deeper within the disclosures.

• Nested Tagging & Regulatory Compliance: AI assists compliance teams by recommending specific regulatory tags (such as financial or thematic ESG markers) across complex text blocks. This ensures the output satisfies regulatory standards for machine-readability.

2. The Consumption Side (How the Market Reads the Report)

The audience for narrative reporting has changed. Institutional investors, analysts, and rating agencies increasingly rely on custom AI agents to read, query, and summarize corporate reports rather than reviewing them manually.

• Conversational Querying: Institutional stakeholders deploy conversational interfaces over parsed filings, allowing them to ask natural-language questions like: "What specific supply chain risks did management highlight in Q3, and how do they contradict the margin outlook?"

• Localization and Synthesis: Advanced translation algorithms dynamically adjust narratives for local regulatory environments, while semantic search tools instantly evaluate the strength of a company’s strategic claims against actual performance data.

Balancing Innovation with Governance

While AI streamlines drafting, structural formatting, and initial analysis, it does not replace executive accountability. Present corporate strategies focus heavily on implementing robust human-in-the-loop validation frameworks. Because narrative disclosures carry significant regulatory weight, AI serves primarily as an analytical drafting partner—allowing human professionals to focus less on manual aggregation and more on clear strategic communication.

Ultimately, companies that combine clean, structured, machine-readable financial data with strategic, human-verified narratives will gain a distinct edge in investor relations and market credibility.

Thank you!

Oracle EPM FCCS: Financial Task Manager

Problem Statement

Manual tracking of the close process, using excel and e-mails is one of the reasons companies still experience issues in their close process from an accuracy, transparency, complexity and comprehensiveness perspective.

By standardizing and embedding controls in period-end close processes, organizations can eliminate labor-intensive, non-value adding activities and streamline decentralized and disjointed workflows that lengthen cycle times, increase the risk of control failure, and threaten the integrity of financial statements.

However, you don’t need to spend countless hours on how to close the books in Real time! 

This article will guide you through how Financial Task Manager is a pathway to Streamline and Accelerate Your Close Operations and best practices you need for a successful journey to EPM Closing and Reporting.

What is Financial Close Cycle

Financial Close Cycle indicates the number of business days required to close the books of accounts and submit finalized financial reports to the management and regulatory authorities at the end of the accounting period. It can be monthly/quarterly/yearly from the time that financial data gathering begins, until management and any regulatory authorities receive finalized financial reports.

Close challenges 

• Inconsistent closing calendar and checklist 

• Lack of quality data from upstream processes or ineffective data aggregation activities

• Lack of automated close processes (e.g., high level of manual journal entries) 

• Ineffective procedures for validating results 

• Lack of timely hard close of periods and ability to post transactions after reporting package submission

• Intercompany accounting – manual adjustments

Organizations are finding it difficult to track:

• The current status of close

• Number of open tasks?

• Where is it stuck?

• Is there any issue in closing the task? 

• Who all are assigned to close tasks?

• Is the predecessor/successor tasks followed?

• Is any assigned employee on Leave?

• Do they need some backup for assigned employee?

• There is no workflow to monitor?

• Approval mechanism in place?

• Looking for email notification for current status?

• Availability of backups?

Task Manager

Oracle Financial Task Manager module of FCCS is built for centralized, web-based management of period-end close activities across the extended financial close cycle.

It helps to manage all financial close cycle tasks, including ledger and sub ledger close, data loading and mapping, financial consolidation, account reconciliation, tax/treasury and internal and external reporting processes.

This includes:

• Close Manager Components (Setup Calendar, Security, Periods, Years, Custom Attributes, Alert Types, Integrations)

• Define Tasks for a Close Process - setup Task Types

• Define Dashboard Views (Calendar, Task List, Gantt Chart)

• Create / Update Templates (and validate) with repeatable close tasks

• Setup / Update Schedule by pulling from a template

• Workflow process

Thank you!

Created by Mohit Jain & Megha Gupta

Ability to Add Instructions to Dashboards in Dashboard 2.0

Prior to release 25.05, there was no provision to enter the instructions for business user for Dashboard 2.0. Below is screenshot from Dashboard 2.0 in designer mode.

   

With 25.05 release, Dashboard designers can now add instructions to entire dashboards in Dashboard 2.0.

Dashboard instructions can be added in the General tab of the Properties Panel in Dashboard Designer, and they can be customized with formatting and URL links. When saved, dashboard instructions can be viewed in the global toolbar in the runtime dashboard.

As part of this enhancement, global dashboard details such as the dashboard name and the folder path of the dashboard have been moved to the runtime dashboard's Actions menu in the Dashboard 2.0 Global Toolbar. Similarly, dashboard component details such as the underlying form name, the folder path of the form, and the cube, can be viewed in the dashboard component's Actions menu.

To add instructions to a dashboard in Dashboard 2.0:

1. On the Home page, click Dashboards.
2. Open an existing Dashboard 2.0 dashboard, or create a new one.
3. In the Dashboard Designer, view the General tab of the Properties Panel. The Instructions property is at the bottom of the General tab. 
4. Click the Disable/Enable toggle next to the Instructions property to set the property to Enable.
5. Enter instructional text (no more than 2000 characters), format it, and then click Save.
6. To view instructions, switch to the runtime dashboard and click Instructions in the runtime dashboard's Global toolbar.

          Click on Disable link to make it change to enable to allow to add instructions

To view details for an entire dashboard in Dashboard 2.0:

1. On the Home page, click Dashboards.
2. Open a Dashboard 2.0 dashboard.
3. On the Global toolbar, click Actions, and then click Show Details.

To view details for a dashboard component in Dashboard 2.0:

1. On the Home page, click Dashboards.
2. Open a Dashboard 2.0 dashboard.
3. Hover over a dashboard component to reveal the Dashboard Component toolbar.
4. Click Actions, and then select Show Details.

This applies to: 

• Enterprise Profitability and Cost Management
• Financial Consolidation and Close
• FreeForm
• Planning 
• Tax Reporting

Business Benefit: This feature allow user to provide customized instructions for entire dashboards in Dashboard 2.0.

Created by Mohit Jain and Megha Gupta

Platform Reports Library Opens Reports and Books Directly in Excel

Prior to this release 25.05, users had to launch the Report in HTML or PDF or launch the Book in PDF and then export to Excel. This required the Report or Book to be run twice. 

From this release 25.05, the Platform Reports library enables user to open Reports and Books directly in Excel via an icon.

This feature streamlines the user interaction and runtime when using Excel with Reports and Books.

It is recommended for users to have Preview POV enabled in Tools > User Preferences > Reports, so users can set the POV before launching the Report or Book in Excel.

This feature applies to: 

• Enterprise Profitability and Cost Management
• Financial Consolidation and Close
• FreeForm
• Planning
• Tax Reporting

Business Benefit: This enables to quickly open the Excel format of Reports and Books by clicking the Excel icon next to the Report. 

Created by Mohit Jain and Megha Gupta

New major update on Task Manager - Data Exchange Integration- Jun-2023 update

Data Exchange Integration Type and Task Type in Task Manager

Task Manager is enhanced with a new end user integration and task type called Data Exchange that allows to streamline the data exchange integration tasks with the monthly close process. This integration is available for:

• Local and remote Financial Consolidation and Close, Tax Reporting, and Planning connections
• Remote Enterprise Profitability and Cost Management

Applies to: Enterprise Profitability and Cost Management, Financial Consolidation and Close, Planning, Planning Modules, Tax Reporting

Benefit: 

• Allows to run integrations defined in Data Exchange from Task Manager.
• This allows the integration of data loads defined in Data Exchange easily into the monthly processing schedule.

Enterprise Journals in FCCS : A Centralize place to manage all Journals

Problem Statement

• Topside Journals taking month end close to infinity.
• In today's business, lots of reliance on offline spreadsheets, differs depending on target ERP systems.
• Many organizations have to make offline workflow mechanism to post journals in GL.
• During Month end close… big question is:
• How can topside journals posted to EPM system travel back to ERP without manually booking them?
• How to automate monthly close to speed up to reduce low value tasks, most of these tasks are offline spreadsheet journals
• Depending on how many ERP systems you have, every Excel template used to process journals may be different.

Many organizations might work on manual or different custom approaches, but these does not meet the requirements as describe below -

Manual approach

• Increases the operational cost
• Significantly increases the risk of material misstatements
• Lack of adequate controls with top-sided JE posting

Custom Integration

• Most customizations are prohibitively expensive
• Do not provide future proofing
• Need of seamless workflow from one process to another

Organizations are looking for a solution who can meet the requirement as depicted below -

This article talks about how Enterprise Journal module provide by EPM Cloud tools to streamline and centralized way to manage manual journal entries across the oracle EPM cloud platform.

What is Enterprise Journal

• It is an EPM platform tool used for preparation, approval and posting of manual journal entries to the journal ledger postings for both Oracle and Non-Oracle ERP systems.
• It provides a streamlined, centralized way to manage manual journal entries across the oracle EPM cloud platform.

How Enterprise Journals can help!

• It addresses challenges mentioned in above section by automating the manual journal entry process for
• Collection
• Posting process
• Centrally managing the process across multiple business processes and potentially multiple ERP systems

Key Capabilities

Below are the key capabilities as below –

1. Single point of entry for all manual journals

2. Standardized journal entry templates based on journal types and general ledger systems

3. The ability to have journal entries validated for -

• Required Fields
• Matching balances
• Proper journal format

4. Visibility of unposed and in-process journals by comprehensive dashboards for Adhoc analysis on status of journals

5. Security rules determine access and assign tasks

6. Posting process is based on the workflow process and defined targets.

7. If target ERP system is Oracle Cloud Financials, Enterprise Journals uses a pre-built direct connector to post to the journal ledger

8. You can use a set of provided APIs for direct posting to other ERPs

9. A file-based journal posting option is also available if required.

Below are the high level steps which are required to setup the Enterprise journal module -

Thank you!

Created by Mohit Jain & Megha Gupta