Here's how data and machine learning are helping businesses create robust identity verification systems

With the increased penetration of digital and automated processes such as KYC, payments and even job applications across platforms and businesses today, there is an abundance of personal information stored on and shared via online channels. But is this data really safe, secure and reliable? There have been several data breaches across platforms and industries over the past few years. This has given fraudsters greater access to the information they may seek to use for identity theft or to falsify other information. In such situations, both consumers and businesses have to be careful when dealing with exchanges of information online.

According to a report by Securitas, identity fraud accounts for more than 70% frauds in India. Moreover, it is a leading cause for a 50% jump in mortgage frauds. For greater transparency and prevention of fraud, it is becoming increasingly necessary to create a system for identity verification that is applicable to all involved parties, while being in-line with government regulations. This can be achieved by incorporating technology such as data analytics as well as artificial intelligence (AI) tools into identity verification systems.

It is important for companies to be able to detect potential fraud before it can actually take place. Fraud detection was previously carried out in the form of audits, meaning it was done after the process was completed. Additionally, this meant most of the negative impact would have already affected the targeted individuals/businesses.

As per a Forrester report, companies around the globe are expected to double their investment in fraud management solutions within the next 3 years, amounting to over USD 10 billion by 2023. Of these, several businesses are implementing machine learning (ML) and data analytics in order to streamline processes to tackle fraud effectively. This helps save them from unpredictable downtime as well as having to shell out billions in compliance damage if fraud were to take place.

ML algorithms are able to analyse data and flag only those transactions which are deemed to be in the high-risk category. In this way, they are able to detect patterns that human teams may not be able to identify, thus being able to predict fraud even before it takes place.

In India, most official identification processes are physical in nature, where customers are required to present a photocopy of their ID cards. With this, there is a higher chance of people having access to and/or presenting forged ID copies and other fraudulent details. Moreover, considering this copy may remain with the service provider, they will also have direct access to personal information such as your address, phone number etc., which is a concern in terms of privacy.

Additionally, there are higher chances of errors while entering data manually, leading to problems in the process at later stages. This also takes many hours of manual labour which, in turn, leads to greater costs involved.

One of the main problems pertaining to identity-related products, especially in India, is the diversity of facial features and the low quality of images provided in ID cards issued by the government. The ideal solution to solve such problems would be to leverage ML and data analytics in order to digitise the identity verification process, which will lower TAT and save costs at the same time. These systems are able to predict fraud based on a set of parameters that help determine the legitimacy of a presented identity. This includes looking for discrepancies in applications, checking for synthetic identities and identifying fraudulent networks.

The functions of ML to streamline identity verification processes are broadly based on three metrics. First, it is to reduce identity fraud and rejection rates. This includes both false acceptance rate (FARs) i.e. Person A being accepted as person B within the system; and false rejection rate (FRRs), i.e. Person A not being accepted by the system as person A.

The second objective is to offer a seamless user experience and providing the shortest possible turn-around-time for transactions involving legitimate customers. This also helps to create a better customer experience, making it more likely that they will follow through with the process. The third use of ML is, given its largely automated nature, it is able to optimise processes and deliver results within a fraction of the time that it would take to do manually. Thus, it helps cut costs and save valuable time.

Fraud can take place in various forms and is generally widespread. As per a 2017 Kroll report, the top targets for fraudsters are customer records, employee records and trade secrets/R&D/IP. While many companies have software and systems in place where teams can carry out identity authentication checks, several complex and hidden patterns are often missed by the human eye.

Therefore, companies require a solution that makes use of AI/ML and computer vision through tools such as object detection, Optical Character Recognition (OCR), facematch etc. These are able to work and learn independently in real-time once they have been programmed.

ML algorithms also have the ability to become smarter with every new piece of data they encounter. This gives organisations the ability to largely automate the analysis of data across multiple devices, applications and transaction formats for accurate inputs.

Using AI tools enables companies to create a robust identity verification system. To ensure these systems work optimally, it is necessary to have access to large pools of relevant data. The data is both obtained as well as processed using data analytics. It is through this data that ML-based systems are able to identify and process photos.

The analysis begins with a predetermined set of rules. These can include tracking multiple identities back to the same device within a specific time-frame or the use of incorrect addresses. To do this, data science and analytics are used to input sets of both true and false data to train the ML system to understand the difference. By using this pre-set data input, ML face-matching and recognition algorithms grow smarter and give better results.

Additionally, identity verification solutions bring together data from internal and external sources. These processes help to carry out a thorough scan as well as curb fraud at multiple levels. The more data the ML system studies, the better decisions it is able to make. By using data analytics, companies are able to attain a high success rate of getting a greater number of people and/or transactions verified digitally while helping to reduce fraud and error rates.

Anyone is susceptible to fraud. However, businesses, in particular, deal with hundreds of employees, transactions and applications on a daily basis. By creating a system that incorporates ML and data analytics, companies will be able to further enhance their identity verification systems. This will give them the ability to detect potential fraud in real-time, and process transactions and applications faster through reliable automation, thus leaving little-to-no room for threats to the business on this front.