Artificial Intelligence (AI) is transforming the banking sector, speeding up processes, making them more efficient, and customer-friendly. From fraud identification to loan approvals, AI is all set to play a crucial role in banking today. The worldwide market for AI in banking is expected to reach a whopping $137 billion (approximately Rs. 11.9 lakh crore) by 2030, which is testament to just how all-encompassing the potential of AI in banking is.
However, implementing AI in banking businesses is not quite as easy as flipping a switch. While big banks and fintech companies are pouring in lots of money into AI, the path to successfully adopting it is not easy. The majority of banks, especially in India, are struggling to implement AI effectively. Why? Because implementing AI comes with technical, ethical, and regulatory issues that need to be handled carefully. Read through the six key issues banks have with adopting AI, and how to overcome them.
1. Availability and quality of data
AI thrives on good data, but most banks still have siloed, outdated and incomplete information. Data scattered across old systems, handwritten records, and digital platforms makes it impossible to train AI models precisely. Various departments of a bank often keep data in silos, which can hinder smooth AI integration.
For instance, if a bank wishes AI to approve loans automatically, but the customer’s half of financial history is not available, then the AI system will not be able to decide correctly. Banks need to spend resources on cleaning, consolidating, and updating storage systems prior to implementing effective AI solutions.
2. High implementation costs
AI is expensive. While the big banks can pay lakhs for AI-based banking solutions, smaller financial services businesses cannot meet the huge expense of infrastructure, training, and maintenance.
AI demands high-performance computing, and hence, banks must replace their old IT systems.
Recruitment of AI experts and training staff to handle AI is costly.
Continuous updating and tracking of AI models add to the cost of operations.
For small and mid-sized banks, investing in AI without an obvious ROI (Return on Investment) is a questionable move. Other financial institutions try to implement AI-as-a-service solutions to cut initial expenses, but sustainability in the long term is still an issue.
3. Regulatory and compliance issues
The banking industry is among the most regulated industries in India. AI implementation needs to meet strict regulations which can decelerate AI implementation.
- AI models deployed for loan sanctioning or detecting fraud need to be explainable and unbiased to prevent discrimination.
- AI needs to be aligned with the Digital Personal Data Protection (DPDP) Act, 2023, so customer data is not abused.
- Banks need to have explainable AI models, that is, they should be able to explain AI-based decisions.
For example, if an AI system rejects a home loan for a customer, the bank should be able to justify why. Otherwise, it might be subject to regulatory attention. Most banks are still learning how to balance AI innovation with adherence to compliance needs.
4. Cybersecurity and AI-driven threats
AI assists banks in avoiding fraud and cyberattacks, but, AI can also turn into a security threat if left uncontrolled. AI-driven cyber threats are becoming more advanced, rendering conventional security solutions less useful. For instance:
- Hackers can trick AI models to circumvent fraud prevention systems.
- Deep-fakes based on AI are increasingly common, rendering it challenging to verify identities.
- Systems can be breached by hackers using AI to approve transactions, leading to financial losses.
To tackle this, banks must invest in AI security frameworks that constantly monitor threats and protect sensitive customer data.
5. Biases in AI models
The performance of an AI model depends significantly on the quality of the data it has been trained upon. If the model is trained on biased data, it can make discriminatory choices, leading to discriminatory lending, false identification of fraud, or unequal customer treatment.
- AI systems can unintentionally prefer certain demographics when they grant loans.
- If it is exposed to historical banking data, AI will end up replicating past biases against some customer segments.
- AI-powered customer service could be biased towards responding to certain questions first, resulting in a sub-optimal banking experience.
A credit scoring system powered by AI that’s based on conventional documents proving income, for instance, could deny loans to gig workers or independent professionals even if they have fixed incomes. Banks need to audit AI models on a periodic basis to ensure unbiased decision-making across different customer segments.
6. Employee resistance to AI implementation
AI is transforming banking operations, but not everyone will be comfortable with it. Some banking staff are afraid of job loss, while others also think that AI-based systems are too complicated to operate.
The most important way to overcome this hurdle is through workforce adaptation. Banks that adapt employees to function with AI, not instead of them, will experience increased rates of acceptance and improved efficiency. AI must be viewed as an assistive tool, supporting employees to make quicker and smarter decisions, not replacing human judgment altogether.
Final thoughts
AI adoption in banking is far from perfect, but its benefits far outweigh its disadvantages. From improving customer experience to preventing fraud and automating compliance, AI integration is clearly the future of banking. However, financial institutions, including NBFCs, must overcome issues like high costs, data privacy, and regulatory hurdles to realise the complete potential of AI.
Online marketplaces use AI to make personalised financial recommendations and detect fraudulent transactions, and they will also need to ensure that these adoption issues are resolved in order to ensure seamless integration with AI. Banks and fintech firms must have the ideal mix of innovation, security, and compliance in place to provide a seamless and trusted AI-based banking experience.