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Payment Clearing and Machine Learning: Everything There Is to Know

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Payment Clearing and Machine Learning: Everything There Is to Know

By Markus Noga, Senior Vice President, Head of Machine Learning, SAP and Martin Naraschewski, Vice President, Head of Finance and Risk Solution Management, SAP

Digital transformation is a phrase that businesses have been using for years. While it feels like we’ve been hearing about digitally transforming for quite some time, the reality is that finance organizations are only experiencing the first impacts of what will be a massive change in the way they work. As the world transitions to the digital economy, there will be a tremendous positive change in the way finance performs its function.

As enterprises become more automated and intelligent, finance is expected to become the knowledge hub of the organization, not only reporting but also simulating outcomes and predicting results. To achieve this, finance leadership should be spending a lot more time in business value creation and driving business strategy rather than providing inputs based on historical numbers.

Thanks to digital transformation, finance is now inextricably linked with (and dependent upon) technology to fulfill almost all objectives around operational efficiency, leadership, and fiduciary responsibility. Machine learning is further accelerating this digital revolution by allowing companies to extract insights, streamline reporting and fuel forecasting without having to program computer systems and set manual parameters. Finance and accounting teams leveraging this technology are now learning to operate intelligently and are moving outside of their transactional role to support timely business decisions with robust planning and simulated functionality.

Machine learning allows organizations to access, analyze and find patterns in Big Data in a way that is often beyond human abilities. While the algorithms that enable machine learning have been around for decades, advances in computing power, data availability and data accessibility are making it possible to incorporate machine learning in more enterprise functions. For accountants, this means simplifying day-to-day activities such as the payment clearing process, so they can focus on tasks where the machine learning models can’t provide exact matches.

Machine Learning in Action

To best illustrate how machine learning can support finance in tasks such as payment clearing, let’s take a look at energy management company Alpiq. Its finance team has been using a traditional rule-based approach for the payment clearing process, making maintaining rules a challenge as they are constantly changing. By moving to a single integrated environment that learns from accountants’ behavior and leverages both historical data and existing AR workflows they will be able to amplify the accounts receivable process by matching incoming payments to open invoices. With the help of intelligently extracted information from payment advice documents and historical data records of successfully matched payments and invoices, they will be able to reduce manual efforts, error-prone and repetitive tasks.

With advanced technology that enables innovative strategies and growth plans, accounting teams have a competitive differentiator that empowers better compliance and reduction in costs that can steer an enterprise toward success.

Overcoming Mundane Clerical Work 

Many accounts receivable (AR) departments struggle to clear invoice payments in situations where customers pay different amounts, do not include sufficient remittance information, or combine multiple invoices in one payment. To clear the invoice, employees must either manually add up various invoices that might match the payment amount, or contact the customer to clarify. Also, an increasing volume of electronic payments means de-coupled remittance information sent separately. In the case of short payment, an employee can either ask for approval to accept the short payment or request the remaining amount from the customer.

With machine learning technology, employees can automate the clearing of payments and receive proposals to match incoming electronic bank payments to open receivables. With the technology, incoming payments are either automatically cleared or a short list of possible clearing matches are suggested that an employee can quickly investigate. Automating this process provides the flexibility needed to move beyond the process of matching and reconciliation of heterogeneous data for more rewarding and higher-value work.

Reducing Costs through Advanced Automation

The traditional rule-based approach to the payment clearing process has become challenging due to constant format changes and the addition of new payment methods. Maintaining rules effectively requires relentless maintenance and excessive costs. This is where using machine-learning-enabled solutions in payment clearing can be advantageous. The technology seamlessly adapts to changing conditions, as it is constantly learning from accountants’ actions, capturing much richer detail of customer and country-specific behavior, without the expense of manually defining detailed rules.

Accounts receivable employees have access to an integrated environment that leverages both historical data and existing workflows – with minimal maintenance required. Leveraging machine learning technology, it adapts to changes automatically, processing incoming payments faster, reducing days sales outstanding (DSO) and improving customer service. Instead of manually reviewing months of spreadsheets, self-learning algorithms can find patterns and solutions in data to make decisions easily, and with confidence. Shared service teams no longer need to spend time updating payment rules and regulations. This freedom enables them to process higher transaction volumes, focus on strategic tasks, and scale with the business by delivering insights and informing decision-making on demand.

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Oil rises on positive forecasts, slow U.S. output restart

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Oil rises on positive forecasts, slow U.S. output restart 1

By Bozorgmehr Sharafedin

LONDON (Reuters) – Oil prices rose on Tuesday, underpinned by the likely easing of COVID-19 lockdowns around the world, positive economic forecasts and lower output as U.S. supplies were slow to return after a deep freeze in Texas shut down crude production.

Brent crude was up 36 cents, or 0.5%, at $65.60 a barrel by 1212 GMT, and U.S. crude rose 39 cents, or 0.6%, to $62.09 a barrel.

Both contracts rose more than $1 earlier in the session.

“Vaccine news is helping oil, as the likely removal of mobility restrictions over the coming months on the back of vaccine rollouts should further boost the oil demand and price recovery,” said UBS oil analyst Giovanni Staunovo.

Commerzbank analyst Eugen Weinberg said optimistic oil price forecasts issued by leading U.S. brokers had also contributed to the latest upswing in prices.

Goldman Sachs expects Brent prices to reach $70 per barrel in the second quarter from the $60 it predicted previously, and $75 in the third quarter from $65 forecast earlier.

Morgan Stanley expects Brent crude to climb to $70 in the third quarter.

“New COVID-19 cases are falling fast globally, mobility statistics are bottoming out and are starting to improve, and in non-OECD countries, refineries are already running as hard as before COVID-19,” Morgan Stanley said in a note.

Bank of America said Brent prices could temporarily spike to $70 per barrel in the second quarter.

Disruptions in Texas caused by last week’s winter storm also supported oil prices. Some U.S. shale producers forecast lower oil output in the first quarter.

Stockpiles of U.S. crude oil and refined products likely declined last week, a preliminary Reuters poll showed on Monday.

A weaker dollar also provided some support to oil as crude prices tend to move inversely to the U.S. currency.

(Reporting by Bozorgmehr Sharafedin in London, additional reporting by Jessica Jaganathan in Singapore; editing by David Evans and John Stonestreet)

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UK-Japan trade deal settled nerves for Japanese firms, Honda executive says

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UK-Japan trade deal settled nerves for Japanese firms, Honda executive says 2

LONDON (Reuters) – Britain’s trade deal with Japan settled the nerves of a lot of Japanese businesses in the United Kingdom and gives them confidence about their future prospects there, a senior Honda executive said on Tuesday.

Japan, the world’s third-largest economy, has since the 1980s made the United Kingdom its favoured European destination for investment, with the likes of Nissan, Toyota and Honda using the country as a launchpad into Europe.

But Britain’s shock 2016 decision to leave the European Union had prompted Japan to express unusually strong public concerns. Their companies and investors warned that a disorderly exit from the EU would force them to rethink their four-decade bet on Britain.

“We welcome very much the Japanese trade agreement which as a Japanese businesses was very welcomed,” Ian Howells, senior vice president at Honda Motor Europe, told a parliamentary committee.

“On the point around confidence, that certainly amongst my peers in Japanese companies was very much welcomed, and probably settled a lot of nerves in terms of their trading prospects in the UK going forward.”

Britain and Japan formally signed a trade agreement in October, marking Britain’s first big post-Brexit deal on trade. It has also made a formal request to join the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP), of which Japan is also a member.

(Reporting by Kate Holton)

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UK retailers see sharp fall in sales and mounting job losses, CBI says

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UK retailers see sharp fall in sales and mounting job losses, CBI says 3

LONDON (Reuters) – British retail sales fell in the year to February as stores cut jobs at a rapid rate, with only supermarkets reporting any growth during the latest COVID-19 lockdown, a survey showed on Thursday.

The Confederation of British Industry’s gauge of retail sales stood at -45, up only slightly from January’s eight-month low of -50. The measure points to falling sales and is below the consensus forecast of -38 in a Reuters poll of economists.

Retailers’ expectations for March – when non-essential shops will remain closed to the public as part of lockdown measures – fell to -62, the lowest since the series began in 1983.

In another sign of a changing consumer habits during lockdown, the survey’s gauge of internet retail sales hit a new record high.

“With lockdown measures still in place, trading conditions remain extremely difficult for retailers,” said Ben Jones, principal economist at the CBI.

“Record growth in internet shopping suggests that retailers’ investments in on-line platforms and click-and-collect services may be paying off, but the re-opening of the sector can’t come soon enough to protect jobs and breathe life back into the sector.”

Job losses among retailers accelerated according to a quarterly question in the survey. For the distribution sector as a whole, which includes wholesalers and car dealers, employment fell at a record rate, the CBI survey showed.

(Reporting by Andy Bruce, editing by David Milliken)

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