Apr 21, 2021

Broadridge: AI’s relevancy will depend on partner ecosystems

AI
Broadridge
ML
partnerecosystems
William Girling
3 min
As AI becomes a necessary tool for staying relevant in today’s market, Neha Singh at Broadridge tells us why establishing partner ecosystems will be vital
As AI becomes a necessary tool for staying relevant in today’s market, Neha Singh at Broadridge tells us why establishing partner ecosystems will be v...

Individual companies’ relative maturity in terms of artificial intelligence (AI) capability is fast becoming an important differentiator. Furthermore, for Neha Singh (NS), Vice President of Innovation and Growth at Broadridge, that gap is widening:

“We have ‘leaders’ in the space with multiple use cases, growing AI adoption, and a greater proportion of spend on AI. They have invested in access to clean data and a modern tech stack and are seeing outsized benefits, increased revenues, and reduced costs vs ‘non-leaders'.”

Therefore, with a desperate need to update and remodel their systems, those companies falling behind need a way to regain their advantage.  

Singh is convinced that developing an ecosystem of external partners will be essential for this objective. In a Q&A session with FinTech Magazine (FM), we find out more about the burgeoning automation revolution.

FM: The utility of AI for FSIs has been demonstrated by COVID-19. What do you expect from the next five years of development?

NS: We expect to see a significant acceleration in AI investment as firms realise strategic benefits, including increased revenues, decreased costs, faster and better decision-making, and effective risk management. 

According to our recent Next-Gen Technology Adoption survey, over the next two years, firms plan to increase their overall IT budgets for next-gen technologies from 12% to 16% on average. Firms will focus on ensuring access to clean data and a modern tech stack to accelerate AI benefits, especially as they move to the cloud.

As they mature in their use of AI, firms will move towards a hybrid approach: business unit or product-driven AI use cases (i.e. decentralised) that are supported by a ‘centre of excellence.’ This will enable organisations to ensure AI delivers commercial value while leveraging AI expertise in a scalable manner.

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FM: With human workers displaced from the more rote aspects of their work by automation, how will they be redistributed?

NS: This shift will create a virtuous cycle that increases productivity and role satisfaction, and enables greater scale in operations. It is critical to manage this transition with empathy and clearly communicate how it can help employees in their day-to-day roles. When Broadridge implemented its digital workforce, we empowered our associates to identify opportunities to automate repetitive and high-volume work, to enable them to do more.

In an interconnected and heavily regulated industry such as financial services, human oversight and the ability for people to make decisions about when to halt digital labour in certain situations will remain key to the governance process.

FM: Please tell me about Broadridge’s 'Centre of Excellence'. Why was it created, what goals have been set and how are you achieving them?

NS: Our AI Centre of Excellence (COE) is composed of data scientists and technologists that use AI/ML techniques to create new products and enhance existing offerings. As a global fintech leader, Broadridge is in a unique position to help clients extract valuable insights from data assets on the company’s platforms and transform the way they do business. 

The COE ensures access to AI talent, instils best practice, and forms a close partnership with business leaders to drive new proofs-of-concept (POCs). This allows us to translate high-level ideas for leveraging AI into tangible impact for clients, quickly and at scale. The COE has helped incubate numerous POCs, many of which are included in our products and services.

FM: How is Broadridge using AI to boost its products and services?

NS: We’re embedding AI into our products and services, allowing clients to realise value in a cost-effective manner. One example is our new corporate bond trading platform, LTX®, which uses AI (LTX AISM) to help broker-dealers automate fixed income trading and maximise liquidity for asset managers. 

Broadridge is also providing banks and wealth management firms with new predictive analytics solutions that can transform their business. By harnessing data in collaboration with our partner Fligoo, we’re able to predict the needs of each client and create a personalised experience that drives client satisfaction and asset growth.

Pictured: Neha Singh, Vice President of Innovation and Growth, Broadridge

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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

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