Even before the rising popularity of ChatGPT and similar tools, the finance industry has begun to explore the use of AI in its various sectors.

According to Bharath Vellore, General Manager, Asia-Pacific, Provenir, in an email interview with e27, there are three notable use cases of AI in the finance industry today:

Identifying Fraud

“A key benefit of using AI for fraud detection is its ability to get smarter with each transaction it processes. So, even when fraudsters evolve their methods, AI models can use real-time data to identify new patterns, learn, and adapt decisioning to maximise the right fraud alerts and minimise false positives,” he explains.

Credit Scoring

“According to our global survey of 400 decision-makers at fintech and financial services organisations, only 18 per cent of participants believe their credit risk models are accurate at least 75 per cent of the time. This uncertainty results in less inclusive credit, fewer approvals and reduced opportunities for business growth,” Vellore says.

“In addition to information that lenders have in their own systems, alternative data from third-party providers such as mobile data, IP and geolocation, identity verification … provides a more holistic view of the applicant’s financial health across the entire customer lifecycle. Alternative data is especially helpful when scoring thin-file and or no-file individuals that have not had credit in the past,” he continues.

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Risk Management

“AI can support the industry in understanding and mitigating risks by analysing complex data and operationalising financial risk models. Through AI-powered platforms, banks can create loan origination experiences that drive customer loyalty,” Vellore points out.

“Other benefits including improving overall efficiency and productivity cannot be overlooked. The ability to generate large amounts of timely and accurate insights will be fundamental in building overall competency around customer intelligence and will be instrumental in providing a vastly superior forecasting ability – which will lead to potentially richer data segmentation. These will essentially be a juncture in maintaining effective risk strategies.”

With these trends identified, we move on to the next question: What challenges remain? How can we continue on improving?

“While AI is being more widely adopted by more companies across a wide spectrum of industries, integration of the technology into real-world applications is not as straightforward – and it is important to recognise that AI is not a ‘magic pill’ solution. Firstly, the technology is rarely a ‘one-size-fits-all’ solution, and while use cases within the same industry may be similar, data integration will be vital,” Vellore elaborates.

“Secondly, organisations also need to consider the infrastructure needed to support an AI solution. For example, are datasets integrated and in a format that is easily accessible to AI platforms? Are sufficient computing resources dedicated to processing to generate real-time insights? All these technology infrastructure requirements need to be addressed alongside implementing AI, unless you partner with a vendor that offers a purpose-built AI platform,” he says.

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Last but not least, Vellore points out that organisations may also struggle with finding the right talent to implement and support AI solutions.

But in the next few years, Vellore says that growth in the use of AI in the finance industry will increase “dramatically”.

“Organisations that can leverage additional data sources and use AI to test and deploy new strategies quickly will be able to better serve their customers, detect fraud, and capture new market share,” he says. “This means that we are likely to see AI being adopted across multiple business units and functions – encompassing everything from credit and risk analysis, and fraud detection to task automation.”

How Provenir seizes these opportunities

Now that we have an understanding of the ongoing and upcoming trends in AI usage in the finance industry, what opportunities does Provenir intend to pursue as a data and AI-powered risk decisioning software?

According to Vellore, credit risk decisioning is becoming prevalent across many industries, such as healthcare, pet care, travel, auto repair, utilities and telco, especially with the growth of BNPL providers.

“Changing demographics also provides an opportunity to modernise decisioning approaches. For example, digital natives, who account for 40 to 50 per cent of all consumption in the region, are more likely to use credit but are less willing to engage in conventional banking. They have always had instant access to services and expect that same level of responsiveness from financial service providers,” he elaborates.

“SMEs are still underserved by traditional institutions or fintech services, yet they represent more than 90 per cent of businesses in APAC. We see a growing number of fintechs serving this audience with automated risk-decisioning technology embedded with AI models that provides instant approvals and funding in hours, not weeks.”

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In the past year, Provenir has achieved several key milestones, including the launch of its AI-powered data and risk decisioning platform and the growth of its marketplace data partner. The company said it achieved 106 per cent growth in revenue and expanding its customer base by 67 per cent in the past year. It has also expanded to include more than 100 data partners, achieving 57 per cent growth over the past year.

Vellore said that the company intends to invest “significantly” in Asia Pacific, especially in Indonesia, the Philippines and Australia.

“These plans include a focus on R&D, growth and most importantly, our customers,” he says.

“Developing new, innovative technology remains a top priority and the company invests 25 per cent of its revenue in R&D each year to ensure that we are always at the curve of development.”

He also shares more about the company’s user acquisition strategy.

“In the spirit of innovation, we have trained our global sales team to be entrepreneurial, crafting targeted solutions for customers and audiences with specific use cases in mind. It is critical that we understand a customer’s objectives and position ourselves as strategic partners to address their challenges and goals. This level of collaboration helps build a customer advocacy to be our very own Provenir ambassador,” he explains.

“We have also been selected to participate in TransUnion’s Strategic Alliance Distribution Program and Visa’s Ready for BNPL program, which allows their clients to take advantage of our industry-leading platform,” he closes.

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Image Credit: Provenir

This article was first published on March 23, 2023

The post Infrastructure, talents are some of the challenges finance industry faces in adopting AI: Provenir appeared first on e27.