Artificial Intelligence. That’s it. That’s the story. The story is on people’s lips worldwide as we engage with the next big thing. AI has crept into every aspect of our lives bringing mixed feelings, and changing lives in the process. Therefore, people would ask: “Will AI take our jobs?” “Can we trust AI?”
You may not get answers to such questions now because everyone wants to eat out of the cake now and won’t focus on the ingredients in the cake. We also have a question: “Can AI work in the banking system or is it going to be a disaster in the nearest future?”, we want to see how the ingredients in the cake work.
AI has become a buzzword in the banking industry over the last few years. It could potentially revolutionise how banks operate, making them more efficient, accurate, and customer-focused.
AI technologies have advanced significantly, and their transformative impact is increasingly evident across industries. For global banking, McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year. Amazing, right? But there’s more to be said.
AI can help improve efficiency, enable a growth agenda, boost differentiation, manage risk and regulatory needs, and positively influence the customer experience. AI-based systems can help banks reduce costs by increasing productivity and making decisions based on information that would seem complex to a human agent.
In simpler terms, AI in Banking:
- Enhances Customer Experience: AI-powered chatbots can provide 24/7 customer service, reduce waiting times and improve customer experience. They can also help banks personalise their services and offer customised solutions based on customer preferences, transaction history, and spending patterns.
- Enables Fraud Detection: AI algorithms can analyse vast amounts of data and detect patterns that may indicate fraudulent activity. This can help banks to prevent fraud and protect their customers’ assets.
- Manages Risks: AI can help banks manage risks more efficiently by analysing data and predicting future trends. This can help them to identify potential risks and take proactive measures to mitigate them.
- Reduces Cost: AI can automate several routine tasks such as data entry, document verification, and transaction processing, reducing the need for manual labour. This can lead to cost savings for banks and improved efficiency.
- Improve Credit Decisions: AI-powered credit scoring models can assess creditworthiness more accurately by analysing a wide range of data sources. This can lead to better credit decisions and reduced credit risk for banks.
The above points emphasize the benefits of AI in banking; however, many banks have struggled to move from experimentation (excluding select use cases) to scaling AI technologies across the organization. Reasons include the lack of a clear strategy for AI, an inflexible and investment-starved technology core, fragmented data assets, and outmoded operating models that hamper collaboration between business and technology teams.
That being said, the banking industry is facing significant challenges and pressures, ranging from increased competition, changing customer preferences, and evolving regulatory requirements. Therefore, in this environment, the adoption of AI technology is no longer an option but a necessity.
However, there are downsides to the AI revolution in banking, including:
- Cybersecurity Risks: AI can be vulnerable to cyber-attacks, and a breach can have severe consequences for banks and their customers. Hackers can exploit vulnerabilities in AI systems, and AI can also be used to launch sophisticated attacks such as ‘deep fake attacks’, that can be used maliciously or to spread false information.
- Job Losses: The automation of routine tasks can lead to job losses in the banking industry. This can be particularly concerning for lower-skilled workers who may find it challenging to adapt to the new technology.
- Biased Algorithms: AI algorithms can be biased based on the data they are trained on, leading to unfair decisions. This can have serious implications, particularly in areas such as lending, where biased algorithms can lead to discrimination.
- Lack of Transparency: AI can be opaque, and it can be difficult to understand how decisions are made. This can make it challenging to identify and rectify any errors or biases in the system.
To mitigate this, however, here are some measures banks can consider:
- Robust cybersecurity measures: Banks should prioritise cybersecurity by implementing strong encryption, multi-factor authentication, and regular security audits. They should also invest in advanced threat detection systems, employee training on cybersecurity best practices, and establish incident response plans to minimise potential breaches.
- Ethical AI practices: Banks should develop and adhere to comprehensive ethical guidelines for AI usage. This includes avoiding biased data, regularly monitoring algorithms for potential biases, and conducting audits to ensure fairness and transparency. Establishing diverse teams to develop and test algorithms can help identify and mitigate biases.
- Regulatory compliance: Banks must comply with relevant regulations and standards related to AI and data privacy. Adhering to these regulations helps ensure customer data privacy and protects against misuse.
- Continuous monitoring and auditing: Banks should implement mechanisms to continuously monitor AI systems for potential issues. Regular audits can help identify biases, improve transparency, and maintain accountability in AI decision-making processes.
- Human Supervision and explainability*: Maintaining human oversight is crucial to ensure accountability and address potential errors or biases in AI algorithms. Banks should also provide clear explanations to customers about how AI-driven decisions are made, enabling transparency and building trust.
- Reskilling and upskilling programs: As AI technology evolves, banks should invest in reskilling and upskilling their workforce to adapt to changing job requirements. This can involve providing training programs to employees, helping them acquire new skills and transition into roles that complement AI technologies.
- Collaboration with regulators and experts: Banks should actively collaborate with regulators, industry associations, and external experts to shape AI governance frameworks. This collaboration can help establish industry-wide standards, guidelines, and best practices to address common challenges.
- Customer education and engagement: Banks should educate customers about the benefits and limitations of AI technologies. Clear communication can help manage customer expectations, address concerns, and build trust in AI-powered banking systems.
By implementing these measures, banks can proactively address the challenges associated with AI in banking and create a more secure, responsible, and transparent AI ecosystem.
To reiterate, the adoption of AI is critical for the banking industry to remain competitive, efficient, and relevant in today’s rapidly changing environment. As AI continues to evolve, it will become increasingly important for banks to keep pace with these advancements to remain successful and sustainable in the long run.
Overall, banks must overcome highlighted obstacles to successfully scale AI technologies across their organisation.
Explainability helps AI users understand the system’s data and conclusions. This empowers people affected by the outcome of the AI system to understand how its suggestion was arrived at.