Revolutionising banking operations with SmartStream's artificial intelligence solutions

The technology can optimise processes in all levels, from reconciliation, to stress testing and payments.

Financial institutions around the Asia Pacific region are rapidly developing their own artificial intelligence (AI) tools to aid their operations as part of the digitalisation strategies that will provide them the leap towards a more digital future. This is something that SmartStream is leveraging in building its own innovations team focussed on developing tools and case studies in the deployment of AI for the use of financial institutions—not only to benefit the experiences of their clients but to also improve the efficiency of their operations.

Push for AI
For Peter Hainz, SmartStream’s global presales programme manager, the decision to implement AI for banks and other financial institutions is something that would be deemed inevitable given the advantages that the technology offers.

“The main driver is cost efficiency, so when we’re looking at, for example, automating the transaction matching process, this is a classical example,” he said. “Another is the regulatory space. So when we’re looking at the regulatory space, we have a lot of data. So, data is collected and used, for example, for stress testing and for analytics.”

He also highlighted the advantages that banks in the region are trying to reap given that 70% of these financial institutions are likely going to use AItools and technologies in their various operations, including collections and recovery by 2019, according to a survey by FICO.

“We can use the data for anomalies with AI. One more driver, I would say, if you’re looking at the operations process, and the user experience is the exceptions management and then routing the exceptions to the right stakeholders,” he explained.

Fast chips, massive data
The push for AI, according to Smartstream, has gone hand in hand with the advancement in technology. “Super fast chips, in combination with massive amounts of data give algorithms a massive boost, so banks have massive amounts of data and this is very important, for example, for data analytics,” he said, although he underscored the fact that AI is a tool and not the be all and end all of things for banks.

“It’s about 80% of getting the data right and 20% of getting the model right,” he said. “What is also very important if we look at, for example, information overload. So, what is fake news, what is real news. Natural language processing is used to extract the right data and to differentiate between fake or real news.”

Optimising workflow through AI
Hainz also shared certain examples on the operational processes of banks and where banks can potentially leverage artificial intelligence. He mentioned that AI, in its purest sense, can affect and improve operations of financial institutions at all levels. AI can optimise the work flow in the back office, including the reconciliation process—where you get the data, transform the data, match the data, and look for exceptions—as well as in cash sweeping.

For the mid-office segment, AI-powered tools and technologies can aid in stress testing activities as well as in supervision. For front-office activities, AI can be leveraged for payment scheduling and payment throttling. Some of the use cases include digital fingerprint, invoice extraction, and looking at anomalies on balances and other financial statements.

SmartStream, for its part, has been offering artificial intelligence-based solutions to banks and other financial institutions through its innovations team, “a collaboration made up of highly skilled members, including mathematicians, applied data scientists, and computer scientists.”

This group is tasked to evaluate and deploy artificial intelligence, machine learning, and blockchain models with financial institutions with cost efficiency and workflow enhancements as the main objectives. Some of the benefits that banks and financial institutions can get from the innovation lab of SmartStream include lower operational risk or the ability to spot anomalies in transactions; enhanced profit efficiency given that routine, reduction in repeatable tasks; and faster response time due to machine learning.

Further, banks stand to benefit from AI by coming into the possession of more informed business insights, and less exposure to processing failures, which will ultimately help any organisation’s decision making process.

They can also leverage on identification of patterns that could provide customised offerings to customers; alerting and learning from unusual transactions. Additionally, it can also help banks stay competitive through the adoption of new algorithms, and better human capital development as the staff are able to direct their focus and resources on more skilful and value-adding tasks.