Oct 7, 2020

Repairing eKYC, AML and the disjointed customer experience

Jumio
KYC
Customer Experience
financial institutions
Dean Nicolls, VP of Global Mar...
3 min
There are five practical steps financial institutions (FIs) can take to lessen the friction between their fraud compliance and user experience teams
There are five practical steps financial institutions (FIs) can take to lessen the friction between their fraud compliance and user experience teams...

There are five practical steps financial institutions (FIs) can take to lessen the friction between their fraud compliance and user experience teams.

Following these steps can help reduce interdepartmental friction, as well as the amount of friction imposed on customers during the onboarding process. These include:

  1. Nix the one-size-fits-all experience. Not all applicants are created equal. Some applicants for a given product are already customers which means you’ve already captured data about their identities and transaction histories. Other applicants may have found your website organically which means you have virtually no information about them. So, each user experience should reflect how much intel you have about the applicant.
  2. Integrate screening and identity proofing. Many FIs treat AML screening and identity verification as two distinct, mutually exclusive processes. But, financial institutions can leverage identity proofing to extract key fields from the ID document such as name, address, date of birth and the applicant’s picture, which can be used to check against PEPs and sanctions lists to help reduce the number of false positives. Fewer false positives translate to fewer cases, lower operational costs and a better user experience.
  3. Higher levels of identity assurance. Many FIs still rely on data-centric approaches for establishing an applicant’s digital identity and this can open the door to money laundering. Data-centric approaches involve checking the identity data (e.g., name, address, phone, date of birth) entered by a user against third-party sources, such as credit bureau data. Thanks to large-scale data breaches, social engineering and the dark web, much of that data has been compromised. Relying on stronger forms of identity assurance, including a picture of a government-issued ID, a corroborating selfie and liveness checks can keep bad actors out of your ecosystem.
  4. Automate, automate, automate. While technology has made it easy to access huge data volumes, it still can’t process data quickly or effectively enough. The problem of speed and accuracy impacts everyone in the chain — regulators, compliance teams, financial institutions and their end customers. While speed is necessary for customer onboarding and for detecting and reporting risk, it also leads to countless false positives that are impossible to check accurately, meaning criminals slip through the net. 
  5. More vendors = more problems. Efficiency is lost using multiple solutions, both with respect to workforce and data. Data gathered during the onboarding process as part of the KYC and customer due diligence process should be leveraged going forward as part of the ongoing KYC program as well as for performing transaction monitoring. For example, a customer’s risk rating, which is created during the onboarding process, can be influenced by how many transaction monitoring cases have been created in their name or how many SARs have been filed on them.

When your teams work together from the outset and focus on delivering a better customer journey, they can build smarter, more flexible processes that produce happier customers, reduced risk and more compliant procedures.

This article was contributed by Dean Nicolls, VP of Global Marketing, Jumio

Share article

Jun 19, 2021

AI and the future of global trade

AI
Tradeteq
trade
Finance
Michael Boguslavsky, Head of A...
3 min
Boguslavsky explores AI's potential in trade finance; could it overcome traditional barriers and usher in a new era of financial transformation?

Artificial intelligence (AI) is becoming entrenched in our daily lives, but the technology is still surrounded by misconceptions and skepticism. Ask the public and they may jump to dystopian scenarios where robots have taken over the world. 

While this makes for a good sci-fi blockbuster plot, the reality is different and more benign. Those products that Amazon suggested you buy? AI. That TV series you were recommended to watch on Netflix? AI. That self-driving Tesla car you crave to take for a spin? You guessed it: AI.

There is no single industry that is not being re-shaped by technology. Until recently, however, there was one noteworthy exception: global trade. Fortunately, that is slowly changing.

The mechanism that underpins global trade – trade finance – is an industry that remains largely paper-based and reliant on manual processes. This US$18tn a year industry is now being influenced by a new wave of technological innovation, including AI.

Exploring the potential of AI in Trade Finance

AI refers to the use of computer-aided systems to help people make decisions or make decisions for them. It relies on large volumes of data and models to make sense of information and draw intelligence. 

In trade finance, AI is helpful in analysing quantitative data, and the repetitive nature of trade finance means that there is a lot of non-traditional data at our disposal. 

This means that when trade finance providers need to assess the risks of funding a transaction, AI models can be a very efficient tool for data analysis and reveal intelligence and risks relating to small companies.

AI helps the industry move beyond traditional credit scoring processes, which are often outdated and remain reliant on historical accounting entries – a barrier that prevents small companies from accessing trade finance and has resulted in a $1.5tn global shortfall. 

Overcoming the barriers

AI can tackle this shortfall by creating accurate credit scoring models. This can include a company’s payment history, measure the risks of funding a transaction, identify supply chain risks, and benchmark them against their peer group.

Trade finance providers can use this information to communicate effectively with their SME clients, ultimately helping establish better business relationships.

Towards a technological utopia?

The adoption of AI has the potential to do a lot of good in the industry, and the industry is in the early stages of radical transformation.

Advances are driven by fintechs as well as a willingness to change. The industry is working together to create new infrastructure for distributing trade finance assets to other investors in a transparent, standardised format. 

The creation of infrastructure is possible due to improvements in technology and integrated across the trade ecosystem in cooperation with banks, insurers, and other industry participants. 

It’s collaboration at its best: together, the industry is using technology to re-shape global trade as we know it.

This article was contributed by Michael Boguslavsky, Head of AI at Tradeteq

Share article