Online Identity Verification – Avoiding Common but Serious Mistakes While Picking a Solution

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23
Sep

Online Identity Verification – Avoiding Common but Serious Mistakes While Picking a Solution

A suitable and effective online identity verification solution reduces fraud, compliance risk and abandonment rates. Going for an online identity verification solution for the sake of managing one’s business is an undebatable choice in today’s world. However, a business leader must carefully survey various solutions available to him / her before picking the most appropriate solution for his business needs. Effective online identity verification can help reduce fraud, abandonment rates, and compliance risk. Researching the available solutions to find the most appropriate one definitely is an important business decision and pays in the long run. The following common mistakes should be avoided which may otherwise have serious consequences:

1. Ignoring one’s specific requirements

A business user must be clear about the set of required information needed to verify the identity of the consumers and the services that his / her business requires. Another important thing needed to be known by the business user are the steps involved in the verification process. Sometimes, an organization makes a deadly mistake by including unnecessary features in developing an identity verification (IDV) solution process which backfires in the long run and for the actual purposes. So clearly the emphasis must be on the few important specifics which shall then determine the right vendor.

2. Ignoring the database size

Solution providers dealing in online identity verification solutions have a huge database. An ID is added to and stored in the vendor’s database every time it is flagged as fraudulent (like being digitally tampered or doctored with). The network effect can have profound overall impact and, as a safe measure, a large parallel database is employed to further and conclusively probe the assessments made by the identity verification tool. For example, if a person has been flagged as fraudulent while setting up a new account with a company, the same person must be flagged when attempting to set up another new account with a different company.

3. Failing to understand machine learning

Contemporary online verification solutions use a number of detection techniques, including machine learning (ML), to filter acceptable IDs from the unacceptable or rejected ones. Machine learning algorithms use large datasets having all kinds of data (in this case accepted and rejected IDs) to train classification models which shall then automatically classify new IDs. As a business manager looking for an identity verification tool, one must be aware of machine learning algorithms and their classification paradigms.

4. Putting too much confidence in automated solutions

Even though many modern identity verification solutions rely on machine learning algorithms and their alluring automation aspects, a business executive must clearly understand that machine learning approaches have inherent limitations and the algorithms generate and train classification models on the basis of existing data. Even though it is a high probability operation, the results are nonetheless of a probable and not a certain nature. It is important to understand and accept the inherent boundaries of automation as a business application tool.

5. Making false assumptions about the vendor’s coverage

False advertising on the part of many identity verification vendors has been responsible for some serious consequences faced by various business in the past. For example, one of these false claims includes the ability to read all kinds of barcodes or MRZ (machine readable zone) of international passports, which is not possible as a MRZ-reading solution cannot necessarily read all forms of government-issued barcoded IDs. Only a well-tested, well-advertised, truly global solution shall be able to read all kinds of barcoded documents as well as be able to scan IDs to match and well-present the barcoded data.

6. Failing or ignoring solution testing before acquisition

Rigorous testing of an IDV solution is advisable before buying a solution. It is imperative to use a large dataset while testing by include thousands or even millions of sample IDs to include maximum aspects of ID verification and to maximize accuracy as much as possible. This way an advanced ID verification solution shall test and approve valid IDs even in poor light.

7. Ignoring user experience

A ID verification solution should be versatile and must guide business executives for ways to solve IDV problems and not just simple transactions. User experience through a rich through easily navigable and a varied interface is a deciding factor for a successful business tool.

8. Putting too much faith in the vendor regarding accuracy

It pays to do research on the implementation and application of an IDV tool for long-term running of a business before settling on and finalizing a vendor. Generally, very few IDV vendors give details on the training algorithm used to determine accuracy of IDV results. So, one should assess and try to measure up the solution on the basis of facts through self-research.

9. Ignoring a multi-platform IDV solution paradigm

IDV solutions must support a number of mediums in today’s world, apart from smartphone image capture, and necessarily include desktop webcams. IDV vendors must necessarily include various platforms like Windows, iOS, PCs, tablets, etc. if they want to capture maximum users. In general all IDV vendors must ensure that their identity verification solution offers the broadest usable spectrum to corporates and business users to bring better traction and thrust to the IDV solution development market.

10. Ignoring or failing to acquire IDV solution technical documentation

IDV solution documentation, like any documented database of a technical solution, allows a business user to understand the solution and its process flow, and thereby evaluate the solution tool for any suitable changes. A comprehensive technical documentation shows clear requirements that are needed to be fulfilled for proper solution development, or in case of any roadblocks faced by the business user team during any IDV checks. Technical documentation is therefore a must for every business team that acquires an IDV solution.

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