Pandemic Disrupted Traditional Banking. And How!
Banks have existed since the dawn of civilization. No wonder such institutions are steeped in legacy systems and are resistant to change, especially technological. However, the COVID-19 pandemic turned out to be a disruptor in chief. The last one and a half years have transformed the way banks operate and interact with their customers. On the other hand, the pandemic further propelled the popularity of fintech firms. To ensure survival, banks had to adapt. And adapt fast. That’s where artificial intelligence came in handy. Applications of AI: Chatbots AI chatbots used by banks and other financial institutions help automate front-end customer service. It allows seamless interactions between banks and customers, replaces human resources for mundane and repetitive tasks, promotes cost efficiency, and reduces turnaround time while being highly scalable. With NLP evolving and domain expertise being added to AI systems, chatbots are now providing seamless services in the front office of banks and other financial institutions, thus driving adoption by the otherwise traditional financial services institutions. Round the clock, automated services by chatbots offer seamless customer services, especially in times like this when physically visiting a bank is a health risk. Chatbots are predicted to see a 3,000% growth between 2019 and 2023. Additionally, Juniper Research suggests banks will be globally saving as much as $7.3 billion by 2023 using chatbots. Automated assessments Usually, banks take ages to disburse loans and process insurance claims since they mostly depend on the human workforce for such tasks. This led to the rise of digital lending and credit startups. As many as 11 fintech startups raised funding during the pandemic. Traditional banks have now batten down the hatches to keep up. Banks today use AI to process loans and insurance claims to reduce the timeline between application and disbursement from months to weeks and often days. Research shows AI has disrupted the insurance claims management system and is expected to save banks as much as $1.3 billion by 2023 compared to $300 million in 2019. Fraud detection With the increasing usage of digital banking, cybersecurity and operational risks have also gone up. “More than a goal scoring business, ours is a goalkeeping business,” said Parikshit Chitalkar, co-founder of the digital lending platform StashFin. This holds for traditional banks as well. Banking systems use ML and Image Recognition Technologies to figure out anomalies in user behaviour and reduce fraud cases. Analysing data to predict future AI can help banks to predict future trends and outcomes. Unlike humans, AI can parse humongous amounts of data to detect anti-money laundering patterns, identify frauds, and also help banks uncover new product and service opportunities. By analysing relevant and existing data sets, banks will be able to generate intelligent recommendations of services and products to their customers, in real-time. AI will help banks automate their processes, making the financial institutions more efficient, cost-saving and seamless, and save human resources for more complex tasks. It’s safe to say the pandemic has become the Chief Digital Officer for many banks. The 2020 Global Corporate Divestment study by EY indicates that 60 percent of the banks intend to divest by the end of this year. Most of these banks will be using the funds to accelerate digital tech adoption such as analytics, AI, robo-advisors and blockchain. Autonomous Next Research suggests banks will be able to reduce their costs by 22 percent by 2030 using AI technologies.
Banks have existed since the dawn of civilization. No wonder such institutions are steeped in legacy systems and are resistant to change, especially technological. However, the COVID-19 pandemic turned out to be a disruptor in chief. The last one and a half years have transformed the way banks operate and interact with their customers. On […]
The pandemic further propelled the popularity of fintech firms.
["AI in banking", "AI in finance"]
["artificial intelligence", "AI", "chatbots", "R", "ML", "image recognition", "AI in finance", "NLP", "Aim", "analytics", "AI in banking", "fraud detection"]
["AI", "artificial intelligence", "ML", "NLP", "analytics", "Aim", "chatbots", "fraud detection", "image recognition", "R"]
After diving deep into the Indian startup ecosystem, Debolina is now a Technology Journalist. When not writing, she is found reading or playing with paint brushes and palette knives. She can be reached at debolina.biswas@analyticsindiamag.com