Improving Payable Processes: An Implementation Primer

Improving Payable Processes: An Implementation Primer

This is a sponsored post by Accusoft. For more information on sponsored contributions please email sponsor@finovate.com.

Accounts payable (AP) processes remain a sticking point for many organizations. Caught between the efficiency issues of paper-based solutions and the potential complexity of adopting technology-driven services, stagnation often results. Accusoft explores its top five tips to smooth out your system and reap the rewards.

Businesses now recognize the necessity of change, but many aren’t sure where to start. When it comes to new permutations of payable processes, a roadmap is invaluable. Here’s a look at five key forms completion and invoice processing improvements to help companies account for evolving AP expectations.

  1. Identifying errors

Staff remain the biggest source of AP errors. There’s no malice here; humans simply aren’t the ideal candidates for repetitive data entry. In this case, effective implementation of new processes depends on customizable software tools capable of accurately capturing forms data and learning over time to better identify and avoid common errors. The benefit? Staff are free to work on time-sensitive AP approval and reviews rather than double-checking basic forms data.

2. Improving invoice routes

Invoice routing is time-consuming and often confusing for AP staff. To avoid potential oversights, most companies use two to three approvers per invoice, creating multiple approval workflows. While the process reduces total error rates, it also introduces new complexity. What happens if invoice versions don’t match or approvers don’t agree on their figures? In the best-case scenario, your company needs extra time to process every invoice. Worst case? Double payment of AP invoices or payments result in missed critical deadlines. Here, a single-application approach to invoice processing helps improve invoice routes and reduce redundant approval steps.

3. Integrating data location

Where is your accounts payable data located? For many companies, there’s no easy answer; some invoices are paper, others are digitally stored on secure servers, and there are still more trapped in emails and messages across your organization. Instead of chasing down AP data, implement an invoice rehoming process. Solutions like Accusoft’s FormSuite for Invoices support thousands of invoice formats and keep them all in the same place.

4. Innovating at speed and scale

Complexity holds back many accounts payable programs. If new technologies complicate existing processes, employee error rates will go up and there’s a chance they’ll avoid digital deployments altogether in favor of familiar paper alternatives. In this case, automation is the key to implementation; speedy solutions capable of scanning paper forms, identifying key data, and then digitally converting this information at scale. 

5. Increasing visibility

You can’t fix what you can’t see. Paper-based invoice processing naturally frustrates visibility by making it difficult to find key documents and assess total financial liabilities. Integrated APIs that work with your existing accounts payable applications can help improve inherent visibility by creating a single source of AP data under the secure umbrella of your corporate IT infrastructure.

Want to learn more about the potential pathways available for companies to improve their AP processes and reduce total complexity? Check out Volume 1 of our Accounts Payable eGuide series, No Pain, No Gain?

Mission-Critical, Concurrent Transactional, and Analytic Processing at Scale

Mission-Critical, Concurrent Transactional, and Analytic Processing at Scale

This is a sponsored blog post by InterSystems, a financial data technology company based in Cambridge, Massachusetts.

Successful financial services organizations today must be able to simultaneously process transactional and analytic workloads at high scale – accommodating billions of transactions per day while supporting thousands of analytic queries per second from hundreds of applications – without incident. The consequences of dropped trades, or worse – a system
failure – can be severe, incurring financial losses and reputational damage of the firm.

InterSystems’ IRIS Data Platform is a hybrid transactional/ analytic processing (HTAP) database platform that delivers the performance of an in-memory database with the reliability and built-in durability of a traditional operational database.

InterSystems IRIS is optimized to concurrently accommodate both very high transactional workloads and a high volume of analytical queries on the transactional data. It does so without compromise, incident, or performance degradation, even during periods of extreme volatility and requires fewer DBAs than other databases. In fact, many installations do not need a dedicated DBA at all.

An open environment for defining business logic and building mobile and/or web-based user interfaces enables rapid development and agile business innovation.

For one leading global investment bank, InterSystems data platform is processing billions of daily transactions, resulting in a 3x to 5x increase in throughput, a 10x increase in performance, and a 75% reduction in operating costs. The application has operated without incident since its inception.

Traditionally, online transaction processing (OLTP) and online analytical processing (OLAP) workloads have been handled independently, by separate databases. However, operating separate databases creates complexity and latency because data must be moved from the OLTP environment to the OLAP environment for analysis. This has led to the development of a new kind of database. In 2014, Gartner coined the term hybrid transaction/analytical processing1, or HTAP, for this new kind of database, which can process both OLTP and OLAP workloads in a single
environment without having to copy the transactional data for analysis.

At the core of InterSystems IRIS is the industry’s only comprehensive, multi-model database that delivers fast transactional and analytic performance without sacrificing scalability, reliability, or security. It supports relational, object-oriented, document, key value, and hierarchical data types, all in a common persistent storage tier.

InterSystems IRIS offers a unique set of features that make it attractive for mission-critical, high-performance transaction management and analytics applications, including:

  • High performance for transactional workloads with built-in guaranteed durability
  • High performance for analytic workloads
  • Lower total cost of ownership

InterSystems IRIS is enabling financial services organizations to process high transactional and analytic workloads concurrently, without compromising either type – using a single platform – with the highest levels
of performance and reliability, even when transaction volumes spike.

Founded in 1978, InterSystems is a privately held company headquartered in Cambridge, Massachusetts (USA), with offices worldwide, and its software products are used daily by millions of people in more than 80 countries. For more information, visit: Financial.InterSystems.com

Synthetic Data Can Conquer FinServ’s Fear of Data Security and Privacy

Synthetic Data Can Conquer FinServ’s Fear of Data Security and Privacy

This is a sponsored blog post by Randy Koch, CEO of ARM Insight, a financial data technology company based in Portland, Oregon. Here, he explores what synthetic data is, and why financial institutions should start taking note.

You’ve heard it before – data is invaluable. The more data your company possesses the more innovation and insights you can bring to your customers, partners and solutions. But financial services organizations, which handle extremely sensitive card data and personally identifiable information (PII), face a difficult data management challenge. These organizations have to navigate how to use their data as an asset to increase efficiencies or reduce operational costs, all while maintaining privacy and security protocols necessary to comply with stringent industry regulations like the Payment Card Industry Data Security Standard (PCI DSS) and the General Data Protection Regulation (GDPR).

It’s a tall order.

We’ve found that by accurately finding and converting sensitive data into a revolutionary new category – synthetic data – financial services organizations can finally use sensitive data to maximize business and cutting-edge technologies, like artificial intelligence and machine learning solutions, without having to worry about compliance, security and privacy.

But first, let’s examine the traditional types of data categorizations and dissect why financial services organizations shouldn’t rely on them to make data safe and usable.

Raw and Anonymous Data – High Security and Privacy Risk

The two most traditional types of data categorization types – raw and anonymous – come with pros and cons. With raw data, all the personally identifiable information (PII) fields for both the consumer (name, social security number, email, phone, etc.) and the associated transaction remain tagged to data. Raw data carries a considerable risk – and institutional regulations and customer terms and conditions mandate strict privacy standards for raw data management. If a hacker or an insider threat were to exfiltrate this type of data, the compliance violations and breach headlines would be dire. To use raw data widely across your organization borders on negligence – regardless of the security solutions you have in place.

And with anonymous data, PII is removed, but the real transaction data remains unchanged. It’s lower risk than raw data and used more often for both external and internal data activities. However, if a data breach occurs, it is very possible to reverse engineer anonymous data to reveal PII. The security, compliance and privacy risks still exist.

Enter A New Data Paradigm – Synthetic Data

Synthetic data is fundamentally new to the financial services industry. Synthetic data is the breakthrough data type that addresses privacy, compliance, reputational, and breach headline risks head-on. Synthetic data mimics real data while removing the identifiable characteristics of the customer, banking institution, and transaction. When properly synthesized, it cannot be reverse engineered, yet it retains all the statistical value of the original data set. Minor and random field changes made to the original data set completely protect the consumer identity and transaction.

With synthetic data, financial institutions can freely use sensitive data to bolster product or service development with virtually zero risks. Organizations that use synthetic data can truly dig down in analytics, including spending for small business users, customer segmentation for marketing, fraud detection trends, or customer loan likelihood, to name just a few applications. Additonally, synthetic data can safely rev up machine learning and artificial intelligence engines with an influx of valuable data to innovate new products, reduce operational costs and produce new business insights.

Most importantly, synthetic data helps fortify internal security in the age of the data breach. Usually, the single largest data security risks for financial institutions is employee misuse or abuse of raw or anonymous data. Organizations can render misuse or abuse moot by using synthetic data.

An Untapped Opportunity

Compared to other industries, financial institutions haven’t jumped on the business opportunities that synthetic data enables. Healthcare technology companies use synthetic data modeled on actual cancer patient data to facilitate more accurate, comprehensive research. In scientific applications, volcanologists use synthetic data to reduce false positives for eruption predictions from 60 percent to 20 percent. And in technology, synthetic data is used for innovations such as removing blur in photos depicting motion and building more robust algorithms to streamline the training of self-driving automobiles.

Financial institutions should take cues from other major industries and consider leveraging synthetic data. This new data categorization type can help organizations effortlessly adhere to the highest security, privacy and compliance standards when transmitting, tracking and storing sensitive data. Industry revolutionaries have already started to recognize how invaluable synthetic data is to their business success, and we’re looking forward to seeing how this new data paradigm changes the financial services industry for the better.