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THE DEFINITIVE GUIDE TO CHOOSING INVOICE DATA ENTRY SERVICES

How to Choose the Right Invoice Data Entry Services?

As companies start to grow, the need for administrative and data-related tasks keeps piling up. You can be a one-man-band or an army of three during the startup stage and cover everything.

However, it’s impossible to keep up all the data entry tasks, especially concerning invoices, once your startup becomes an established brand. Adding more workers in-house is too much of a hassle and costs a lot, so having your data entry outsourced is the best solution.

But how do you find the right company to take care of this for you? Here are some critical things to consider.

 

Seven Essential Qualities of a Good Invoice Data Entry Service Company

Below you’ll find a concise list of the essential qualities you want in a company that offers invoice data entry services.

 

1. Precise and Double-Checked Data
Any data entry company worth their salt will have safety measures in place to ensure that their data is accurate and error-free. This include:

  • Double key data entry – when another data entry specialist goes over the same material to ensure a high degree of accuracy.
  • Data validation – the source of the data is checked to ensure that it is clean and consistent before proceeding with data entry.
  • Data verification – once the data is transferred to the file that will be sent to the client, it is checked for accuracy and any inconsistencies once more.

With all these fail-safes in place, a good company ensures that you get exactly what you need when you have your data entry outsourced.

 

2. Deep Understanding of the Latest Industry Standards
Each industry has its unique jargon, and it takes a bit of experience to understand it fully. Mistakes that seem small can often drastically change the meaning, and one wrong number can often have catastrophic consequences. Therefore, a competent and skilled data entry specialist will be up to date on all the industry news and standards. This helps them avoid any errors in terms of spelling and grammar.

 

3. High Level of Cybersecurity
The last thing you need is to worry about a data leak when outsourcing routine invoice data entry tasks. A professional data entry company should have solid encryption. This involves using a VPN connection, having an SSL certificate, and so on. And they should be forthcoming about it.

Be wary of any company that doesn’t seem to take cybersecurity seriously enough or tries to avoid any questions about it.

 

4. Saves Time and Money So That You Can Invest in Key Abilities
As we mentioned, having in-house staff handle data entry can be time-consuming and expensive. It can also take your focus away from the critical tasks that your company excels at.

An efficient outsource company will get the job done without too many questions or revisions. This way, you get to focus on improving your products and services, marketing, and making more money.

 

5. A Large and Highly Qualified Talent Pool
There are a lot of small outsource companies that are no more than a group of freelancers huddled up in a coworking space.

Such businesses only have 5-6 workers and can be slow and inaccurate. What you need is an established company with a good reputation. With outsourcing partners like these, you’ll have access to a wide talent pool.

The benefits include:

  • Exceptional work ethics
  • Open to tighter deadlines
  • Can scale their service as your demands grow
  • Less back-and-forth to fix mistakes

Look out for reputable businesses with positive reviews and a large workforce.

 

6. Uses Only the Most Up-to-Date Tools
As we’ve seen, if you want to do invoice data entry services the right way, you have to cover everything – data security, check and re-check for accuracy, and so on. And for all this work to be done accurately, you need the latest tech tools.

These include:

  • Cloud technology – for useful data storage and ease of access and sharing.
  • Process automation tools – that increase efficiency and reduce the chances of human error.
  • Security software – that keeps the client’s data safe and avoid breaches that would lead to loss of progress and delays.

Be sure to ask your data entry company about the technology they use and go with those who use the latest tools and apps.

 

7. Ensures Optimal Effectiveness and Morale
The crucial piece of information you need from an outsource company is how fast they can get the job done. The next big question is how much work they can take on. And both these things depend heavily on the productivity of their team.

A lot of companies will boast about their productivity, but very few can deliver on their promises. Look for a company whose invoice data entry services are top-notch, not just one that offers suspiciously short deadlines.

Also, a company that is proud to talk about its company culture and its “team,” “experts,” “dedicated staff” is a safe bet. Such companies tend to treat their workers well and offer incentives to boost productivity. This means that employee morale is probably high and that everyone is motivated to do a good job.

 

 

Conclusion

Data entry is one of those seemingly mundane, yet incredibly essential tasks that every serious company needs. If you are well past your startup phase, you’ll find yourself in need of accurate invoice data each week.

And since these things take extra time and effort that you don’t have, it’s best to outsource invoice data entry services. Just go over our list of seven things great data entry outsourcing companies should have, and you’ll find the right one.

Don’t be afraid to ask questions and take the time to talk to several companies. After all, it’s your reputation that’s on the line with every invoice you send, so choose wisely.

 

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Business

HARNESSING ANALYTICS IN THE FIGHT AGAINST FRAUD

ANALYTICS

By Anna Lykourina, EMEA Fraud Analytics Expert at SAS

 

In the past, the fight against fraud has been a bit hit-and-miss. It has relied on auditors to identify patterns of behaviour that just didn’t quite fit. They often only detected problems months after the event. And then organisations had to claw back stolen funds through legal processes.

In a world where transactions happen in under a second, however, this is no longer acceptable. We need to be able to detect fraud immediately, if not before it happens. Customers want safe and protected data that is not vulnerable to identity theft through company systems. But they still want to be able to pay online and in seconds. The stakes are high, but fortunately new tools and techniques in fraud analytics are enabling companies to stay ahead of fraud.

 

Trusting machines to do the work

Machines are much better than humans at processing large data sets. They are able to examine large numbers of transactions and recognise thousands of fraud patterns instead of the few captured by creating rules. On the other hand, fraudsters have become adept at finding loopholes. Whatever rules you set, it is likely that they will be able to get ahead of them. But what if your system was able to think for itself, at least to a certain extent?

New approaches to fraud prevention combine rules-based systems with machine learning and artificial intelligence-based fraud detection systems. These hybrid systems are able to detect and recognise thousands of fraud patterns and learn from the data. Automated analytical-based fraud detection systems can reveal novel fraud patterns and identify organised crime more consistently, efficiently and quickly. This makes them a good investment for businesses across a wide range of sectors, including public sector, insurance, banking, and even healthcare or telecommunications.

How, though, can you harness analytics as a tool in your fight against fraud?

 

Identifying needs and solutions

The first step is to identify which options you need. Probably the best way to do this is through a series of company-wide workshops with the fraud analytics experts to determine what analytics you need, which data to include and techniques to use, and what results to report. They can also identify the ideal combination of rules-based and AI/ML approaches to detect fraud as early as possible.

Companies looking towards advanced analytics for fraud detection will need to make a number of decisions. They will need to optimise existing scenario threshold tuning, explore big data, develop and interpret machine learning models for fraud, discover relevant information in text data, and prioritise and auto-route alerts. There may be industry-specific decisions to make, too, such as automating damage analysis through image recognition in the insurance sector. By automating these areas, companies can both significantly reduce human effort – reducing costs – and improve their fraud detection and prevention.

 

Benefits of an analytical approach to fraud detection and prevention

Companies that are already using an analytical approach for fraud prevention have reported several important benefits. First, the quality of referrals for further investigation is better. Investigators also have a much clearer idea of why the referral has been made, which improves the efficiency of investigation. Analytics also improves investigation efficiency by reducing the number of both false positives (that is, alerts that turn out not to be fraud) and false negatives (failure to spot actual frauds). This improves customer experience and reduces risk to the company.

Analytics makes it possible to uncover complex or organised fraud that rules-based systems would miss. Companies can group together customers and accounts with similar behaviors, and then set risk-based thresholds appropriate for each scenario.

There are several sector-specific benefits too. For example, insurance firms can identify fraudulent claims faster to prevent improper payments from going out. Claims investigation is likely to be more consistent because claims are scored through technology, algorithms and analytics, rather than by people. Finally, it becomes possible to shorten the claims process through automated damage analysis. It is no wonder that organizations across a wide range of sectors are placing analytics at the heart of their anti-fraud strategy.

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Business

2020 VISION: TRANSFORMING THE LEGAL DOCUMENTATION LANDSCAPE THROUGH STRUCTURED DATA

STRUCTURED DATA

Jason Pugh, Managing Director, D2 Legal Technology

 

The derivatives industry has been transformed by the proactive engagement of its members over the last 30+ years, an exemplar of bright, resourceful individuals coming together to achieve business outcomes that benefit the industry as a whole. From pioneering the master agreement, the eye-catching creation of protocols, to harmonisation of business process through the likes of FpML, the industry has constantly evolved.

Today, the industry is facing new challenges and while many will consider, correctly, that the proliferation of global and regional regulations since the financial crisis has both been challenging and led to unintended consequences, there is an even more stark reality that players in this market need to consider, i.e. surviving in a disrupted universe.

 

Jason Pugh, Managing Director, D2 Legal Technology, outlines the potential that can be achieved by enhancing legal data standards and how that this is an essential precondition to fundamentally transforming the operating environment through technology.

 

We all witness the impact of Uber and Amazon in every walk of life which has extended client expectations. We all know that as clients appreciate and come to expect these new capabilities and services, disrupted technology will not be put back into the bottle.

Similarly, clients in the financial services industry rightly expect more for less. It may also be less complex than we fear – the industry is, after all, not as unique as it likes to think and a vast proportion of our business can be commoditised.

The critical challenge for the industry is therefore to transform itself into a cheaper and better risk managed operation that achieves the twin goals of client satisfaction and regulatory compliance – this means simplification, the current framework is too complex comprising too many disparate processes pieced together in a makeshift manner.

The correlation between better client service, better risk management/compliance and cost efficiency is high when viewed through the prism of effective front to back processes. This is the challenge the industry faces, and the good news is that many of the strands are already being developed; the challenge is to bring them together.

 

The journey so far

Over these last decades, ISDA has worked with its members and market participants to produce and maintain a documentation framework. It has constantly responded to market changes and this has led to an evolution of its suite of documentation especially with the development of the ISDA Master Agreement and associated documentation, such as various annexes, definitional booklets and protocols. This framework has provided important legal certainty, clarity and efficiency for market participants and critically transformed the credit risk profile of trading entities through the concept of close-out netting.

In recent years, the number of standard form documents and their complexity has proliferated often in response to regulatory requirements. Many of the core terms have remained constant, yet there has been an ever-increasing number of variants in the specific clauses used within the documentation framework, increasing the time taken for negotiation and onboarding of new client relationships.  These increased variances have different commercial and operational effects and have precipitated multiple bespoke business processes to monitor, at a time when monitoring has been more scrutinised than ever, post financial crisis.

The increased cost of supporting pre- and post-trade activities and complying with the new regulatory obligations, alongside reduced profit margins in the derivatives business, is not sustainable. Against a backdrop of an increasingly digital and data-driven world, there is a need and an opportunity to standardise and digitise the legal documentation.

Through the adoption of common market standards, the market will be able to leverage technology-enabled contract delivery and management solutions, as well as allow the use of technology such as Distributed Ledger Technology (DLT) and smart derivatives contracts.

Significant work has progressed in these areas through the work of ISDA and others and there is a broader recognition of the need for market infrastructure, utilities, data governance, documentation change and process change. However, there is more to be done and until recently, legal agreement clause/data standardisation and legal agreement data had been at the periphery of current legal technology initiatives. But it is now falling into the mainstream, with clause taxonomies which are designed to address the growth of clause variants into one singular vernacular. Most importantly, we have seen the development of an outcomes based approach where variants are being condensed if they relate to the same business outcome. This is foundational when looking to enhance process, reduce risk and meet client expectations.

 

A glimpse of what “strategic state” looks like

Historically, written legal agreements have been king as we look to document and evidence the intention of trading parties, which has been largely effective. However, the legal profession has, on occasion, complicated contracts through verbose legalese that is not even consistent with the prose of other lawyers and incomprehensible to the uninitiated – never mind those e.g. in operations, giving effect and managing the risks arising from the contractual obligations they create.

The environment has changed and in an increasingly data-driven world, it is no longer the written word that is king. Firms are moving to operationalise their businesses through automated data-driven processes, and accordingly, key commercial and operational terms, as well as risks monitored within legal agreements need to form a part of the business process if they are to play a part in optimising the business decision-making, management of commercial risks and operations. However, until the key data elements of the legal agreements are structured, transparent and consumable, this optimisation is impossible. This means defining standard structured data variables and allowable values for those defined variables.

 

It all starts with structured data

We are on an inevitable journey to data-orientated legal agreements, with a representation of the written contractual terms in a manner that follows a consistent, predictable and structured data format. There are numerous tangible benefits to data orientated contracts, such as enhancing the process of negotiating legal agreements, allowing the opportunity to automate the creation and delivery of legal agreement documentation, and negotiate and execute it with multiple counterparties simultaneously, by focusing on intended business outcomes.

By having a standard list of variants focusing on outcomes of those clauses, it is possible to utilise LegalTech solutions to parse through legacy legal agreement documentation, and classify the clauses contained within such documentation against those standards and successfully manage those contractual obligations to optimise the business.

 

Challenges on the road to delivery

Markets and industries, by their very nature, tend to resist new ideas, products and standards. Added to this is the sheer amount of change to the pre- and post-trade processing and market infrastructure landscape in OTC derivatives following the 2008 financial crisis.

However, to unlock the benefits of the changing legal documentation landscape, the focus needs to be on data. Firms have historically under-invested in core reference data, and whilst there have been marked improvements, the standard is lacking for legal contract data; firms are simply unable to systematically understand the risks emanating from their broad contractual portfolio.

Clause taxonomies create a framework in which to work with legal agreements and manage the contractual obligations they contain, allowing classification to be conducted within the framework of that taxonomy. Although taxonomies are a well-established approach to categorising and linking to business processes, these have only been used to a limited extent by market participants for legal agreement management, and typically created individually (often for a particular department or specific use within a firm). They do however, form the foundations of optimising value from business processes and unlocking value through (legal) change.

 

Conclusion

Market participants have demonstrated considerable pioneering spirit to develop the industry through legal documentation. It now needs to be bold enough to take the next step to unlocking the digital agenda by developing common data standards. There are times when firms should compete and there are times when they should converge for the common good – and this in one of them.

Structured data will enable technology to provide the insights clients require with a far simpler and more sustainable operating model. We therefore need to think smart and adapt to operate in this new landscape which we should embrace, rather than resist.

 

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