Analytical Ledger

[Lutgarda Briseno]

TheCommittee on Payments and Market Infrastructures has published a report on Distributed ledger technology in payment clearing and settlement. The report provides an analytical framework for central banks and other authorities to review and analyse the use of distributed ledgers in payment, clearing and settlement activities. Market participants may also find the report useful.

The framework focuses on the potential implications for efficiency and safety and for the broader financial markets. The framework is directed primarily at arrangements that involve restricted ledgers (ie access to which is for approved users only), reflecting the main types of arrangement currently being developed in the financial sector, which are of particular interest to the relevant authorities.

General Ledger Review is the process of analyzing your department's ledger transactions to provide reasonable assurance that the charges and credits are valid. In addition, the reviewer verifies that transactions are appropriate and that they are compliant with all applicable policies and regulations. It is also a requirement of UCD PPM 330-11: Approval Authorization for Department Transactions.

It is our obligation to be good stewards of the university funds that have been entrusted to us. As a recipient of funds from a variety of sources, the university must demonstrate to public and private organizations, taxpayers, donors, and anyone else who provides us with funding that we are using the money as intended. Ledger review impacts the university's annual financial schedules and provides accurate information for further financial analysis to occur at higher departmental, divisional, school, and college levels.

If you have questions or concerns about transactions that have posted to your accounts please contact one of the following:

> Accounting & Financial Reporting

> Contracts & Grants Accounting

> Controls & Accountability

Pennylane users, both accountants and their clients, can add analytical tags to their bookkeeping entries and/or invoices and bank transactions. When a bookkeeping entry or a document is tagged, the tags are inherited by the corresponding ledger event, and become available in the analytical ledger.

Multiple tags can be associated with a single ledger event, with varying weights. In such situations, the same ledger event will appear several times in the analytical ledger, once for each tag associated with it.

A thirdparty represents a supplier or a customer of the company managed on Pennylane. Third parties can be both legal entities or individuals. The thirdparty_id is the unique identifier of the third party on Pennylane, and refers to suppliers.id or customers.id depending on the type of third party.

If the bookkeeping entry is associated with a third party, thirdparty_id is its ID.

Otherwise, thirdparty_id is inherited from the document associated with the bookkeeping entry in case of 4xx (invoices) and 5xx (bank transactions) accounts.

Improve payables and receivables efficiency by closelyexamining the composition of working capital. Analyzeperformance and identify opportunities to improve the use ofworking capital with prebuilt or custom metrics based onuser-defined benchmarks, without the assistance of dataanalysts, database admins, or IT.

Quickly identify inefficiencies within the collectionsprocess with prebuilt views of customers with overduereceipts, delinquent accounts, and billing issues. Reviewadjustments, write-offs, and unapplied receipts using aunified view for faster reconciliation and cash collections.

Use prebuilt performance indicators to easily analyze agingreceivables using historical trend data displayed bycustomer, GL account, GL balancing segments, and otherfactors to understand the impact on collections.

Visually prioritize upcoming AP invoices that are due withina specific time range or other predefined parameters, andeasily customize views without IT intervention. Understandthe root cause of invoice holds to help improve DPO.

Review AP aging buckets using historical trend analyses byvendor, GL account, GL balancing segments, and otherfactors. Extend and customize the prebuilt data pipelines,models and KPIs to tailor analytics to your organization.

Quickly analyze organizational spend across differentcategories, including business unit, geography, and costcenter with prebuilt, performance indicators acrossdepartments. Develop strategies to consolidate suppliers andnegotiate better pricing terms using detailed insights intopurchasing patterns.

View finance and procurement metrics together to optimizeworking capital without complex data integration or helpfrom IT. Optimize supplier payment timing by performingcost-benefit analysis to capture unrealized interest and/orearly payment discounts.

Oracle ERP Analytics prebuilt data visualizations allowfaster and easier identification of expense anomalies toquickly remediate fraudulent activity and policy violationsand prevent future occurrences.

Use prebuilt analytics and data visualizations to trackoff-contract spend patterns, agreement leakage, andagreement utilization by historical trends, item, andsuppliers to identify potential savings opportunities.

Analyze supplier performance against contractual benchmarksusing multiple prebuilt metrics, such as on-time delivery,rejected, shipped, short-closed, and return rates. Quicklyidentify the root cause of poor supplier performance tominimize risk and disruptions to the business.

Visually explore revenue and billing trends across projects and customers witheasy-to-use, self-service visualizations. Drill down to granular details to viewaccounted distributions by GL account and period to identify unaccountedtransactions.

Get connected project insights with a single, analytical, extensible data modelacross Fusion Analytics that increases visibility of your project data, bridgingdepartments. Quickly find correlations by analyzing timecards, sales orders, andinventory transactions with project attributes.

Combine Accounting Hub data with Projects, General Ledger, Payables, andReceivables for 360-degree visibility. Easily combine data from third-partysources such as operational and digital systems with accounting data.

General Ledger analysis provides a means of distributing transaction values to ledger accounts together with analysis information. This reduces the need to create individual ledger accounts for certain categories of expenses such as travelling, repairs and maintenance, general expenses or donations for example. It also eliminates the need to manually analyze such General Ledger accounts.

GL analysis entries are only posted from sub modules if the sub module is linked to the General Ledger in detail at company level (General Ledger Integration) or at ledger account code level (General Ledger Codes or GL Structure Definition).

You will not be required to enter GL analysis entries for accounts defined in the General Ledger Integration setup programs for General Ledger integration. This means that when you process a transaction and an account specified in a General Ledger Integration setup program is automatically used in the transaction (i.e. you do not manually enter it) then the analysis requirements are ignored for that ledger code.

When you define an account type as requiring analysis and you use the General Ledger Codes program to add a ledger code with that account type, a message is displayed to remind you that analysis may be required for that ledger code.

Use either the General Ledger Codes or GL Structure Definition program to indicate the ledger accounts against which analysis is required and to define the analysis category to use for that specific ledger code.

In addition, if you want to process analysis entries from sub modules, then the General Ledger integration method for the sub module(s) must be set to detail either at company level (General Ledger Integration) or against the individual General Ledger account code(s) (General Ledger Codes or GL Structure Definition). Failure to do this results in analysis entries not being posted.

Whenever you process a transaction containing a General Ledger code for which G/L analysis is required (see General Ledger Codes or GL Structure Definition) then the program is displayed either when you post the transaction, or when the transaction value is distributed to one or more ledger codes.

In this example, all five category types were used. You could, however have combined FUEL and OILS into one category type. Alternatively, you could have defined only one category type: REGISTRATION and processed all expenses for a specific vehicle (irrespective of the type of the expense) to that vehicle's registration number.

Procurement analytics typically involves collecting data from various source systems and ERPs. Data is then classified into standard or use-case-specific taxonomies. After classification, the data is presented in a visualization dashboard or within business intelligence tools.

The need for procurement analytics has developed from the desire to get a consolidated view of procurement spend. Over the years, it has developed from one-off projects like spend cubes to cover a number of specialized solutions, dashboards, and types of automation software.

In this guide, we'll cover the basics of how procurement analytics can be applied in your organization. We'll give you insights into use cases, KPIs, how procurement analytics is built, and how to choose a solution. Enjoy!

Procurement analytics is not only for procurement. It provides value to the whole organization. All other functions, from marketing to finance, can benefit from procurement data and its broad range of insights.

Procurement organizations can utilize analytics to describe, predict or improve business performance. It can enable effective and data-driven decision-making. Automation of repetitive tasks in procurement leaves more time and focus for strategic decision-making and relationship management.

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