Financial institutions have been at the forefront of technology adoption and digital transformation, and now AI is playing a significant role in bringing speed and depth to a myriad of complex and time-consuming test processes.
According to McKinsey’s 2023 banking report, Generative AI has the potential to enhance productivity in the banking sector by up to 5% and reduce global expenditures by up to $300 billion.
“But that is just half the picture,” said Arindam Ray, a digital transformation consultant, specialised in the financial services space and in particular banktech applications and finserv-focused software solutions.
Case in point, Generative AI models are being used to summarise lengthy regulatory reports, simplifying compliance tasks for banks, he pointed out.
“Transfer learning models being used for advanced chatbots capable of generating human-like responses to customer queries, as well as assisting in drafting financial reports and emails,” he added.
Referring to Sam Altman, the CEO of OpenAI, Ray recalled that he recently spoke about how humanity is on the brink of a transformative era with AI set to revolutionise daily life in ways never seen before, and how banking will be at the core of it.
Ray sees a host of benefits, such as banks employing AI systems for automating primary tasks such as credit scoring and fraud detection.
“The integration of AI with blockchain technology is poised to restructure banking software as we know it.”
– Arindam Ray
Since 2010, machine learning (ML) brought more advanced systems that could learn from data and improve over time.
“Banks began using ML algorithms to analyse customer behaviour and build models for credit risk assessments and fraud detection, providing a more proactive approach than conventional methods,” Ray noted.
“However, AI’s role remained largely operational, specialising in backend improvements without directly influencing customer experience,” he said.
Having said that, “today, AI has far-reaching impact in banking.”
AI banking apps
Zooming in on several current AI applications in banking, Ray singled out real-time onboarding.
“One of the noteworthy developments in AI at the service of the bank is the real-time onboarding of clients.”
As per a study from McKinsey, AI can bring onboarding time down to over 50%, thus improving customer experience and enabling banks to get more applications in a shorter span.
“If done well, applying AI can enable banks to onboard retail and corporate customers in a few clicks, completing the process in near real-time,” Ray said, as he added that it will automate many software processes and automation procedures, thereby enhancing any bank’s software capabilities.
Then there is fraud detection: from a rules-based approach, fraud detection techniques have improved to the extent where it is now possible to identify frauds real-time and rely on models rather than call-to-action protocols.
“A typical modern-day fraud detection unit is capable of scanning extensive databases containing transaction data to extract unusual activities,” Ray said.
Blockchain
The real win for banks, in the years to come, may be merging AI and Blockchain for, Ray stressed.
“The integration of AI with blockchain technology is poised to restructure banking software as we know it,” he stated, adding it could give software testing and the integration of new digital infrastructure ecosystems a major boost.
“Blockchain’s shared ledger boosts security and transparency, while AI further enhances these advantages by accelerating testing processes, improving accuracy, and more effectively detecting fraud,” Ray explained.
“AI can analyse blockchain transactions instantly for efficient fraud prevention. It can also improve smart contracts, self-running agreements with built-in terms, by automating complex financial tasks, reducing the need for middlemen, and boosting testing and efficiency,” he continued.
“Blockchain’s shared ledger boosts security and transparency, while AI further enhances these advantages by accelerating testing processes.”
– Arindam Ray
However, there are also challenges in deploying AI solutions in banking, Ray acknowledged.
One of the biggest areas of concern is the ethical use of AI.
As AI becomes increasingly prevalent in banking, it is essential to address ethical concerns such as transparency and bias, Ray said.
“Decisions made by AI must be clear and understandable to maintain customer trust. Explainable AI is emerging as a solution, offering transparent reasons for decisions,” he said.
Ray stressed that AI bias is a major issue.
“Without careful oversight, AI systems can continue existing biases, especially in credit scoring and fraud detection. Historical data may have biases that influence AI decisions,” he noted.
As AI technology develops, it’s important to ensure fairness, transparency, and accountability, Ray continued.
In summary, he is convinced AI will play a pivotal role from an engineering point of view.
“For banks, the opportunities are endless. With rapid growth in the financial ecosystem, the banking sector’s embrace of AI demonstrates its potential to drive innovation, improve efficiency, and create a more inclusive and AI-first ecosystem,” Ray concluded.
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