Hyper-personalization is fast becoming a core component of the financial services industry, It has risen rapidly in prominence thanks to the emergence of machine learning and AI technologies, allowing banks to tailor their offering specifically to customers and their exact requests. Indeed, harnessed correctly, hyper-personalization can enable financial services providers to interact with their customers
To say that the fintech market is rapidly growing could well be one of the biggest understatements ever witnessed.
Indeed, research highlights the staggering growth that the global fintech market is expected to undergo. It attained a value of approximately USD 194.1 billion in 2022 and is expected to grow from 2023 to 2028 at a CAGR of 16.8% to reach USD 492.81 billion by 2028.
We only need to flick through the money pages of national newspapers to remind ourselves just how much figures like this are impacting the financial status quo. With never-ending news about high street banks closing branches because of a lack of sustainable footfall through their front doors, the future is certainly digital.
The Covid 19 pandemic was the catalyst that spurred many providers into “going digital” with even very traditional building societies and credit unions now offering online functionality and smartphone apps. However, even these developments are beginning to look somewhat dated when compared to the cutting-edge functionality that is now being offered by the latest generation of AI and hyper-connected systems.
AI now touches virtually every aspect of banking, including customer support, risk management, compliance, loan management, credit scoring, and marketing.
How does AI help in banking?
AI is transforming the banking industry by streamlining operations, enhancing customer experiences, improving risk management, and enabling innovative financial services. Here’s how AI is driving disruption across key areas in banking:
1. Customer service and personalization
- Chatbots and virtual assistants: AI-powered tools like chatbots handle routine inquiries, account management, and troubleshooting, offering 24/7 support while reducing the need for human agents.
- Personalized recommendations: AI analyzes customer data to suggest tailored financial products, investment opportunities, or budgeting tips, improving customer engagement and satisfaction.
2. Fraud detection and security
- Real-time fraud monitoring: AI systems detect anomalies in transaction patterns, identifying and blocking fraudulent activities faster than traditional methods.
- Biometric security: AI-driven technologies like facial recognition and voice authentication enhance security in accessing banking services.
3. Credit scoring and lending
- Enhanced credit assessments: AI models evaluate creditworthiness using alternative data sources, enabling more accurate risk assessments and increasing access to credit for underbanked populations.
- Automated loan processing: AI streamlines the loan approval process by automating document verification and credit checks, reducing turnaround time.
4. Risk management
- Predictive analytics: AI predicts market trends, customer behaviour, and potential risks, helping banks make informed decisions and avoid losses.
- Stress testing: AI models simulate economic scenarios to assess a bank’s resilience under adverse conditions.
5. Operations efficiency
- Process automation: Robotic Process Automation (RPA) powered by AI handles repetitive tasks like data entry, compliance reporting, and account reconciliation, cutting costs and minimizing errors.
- Document analysis: AI systems extract and analyze data from complex documents, speeding up processes like Know Your Customer (KYC) compliance.
6. Investment and wealth management
- Robo-advisors: AI-driven robo-advisors provide low-cost, automated investment management services, making wealth management accessible to more customers.
- Market insights: AI algorithms analyze vast datasets to generate actionable insights for trading and portfolio management.
7. Customer acquisition and retention
- Targeted marketing: AI identifies customer segments and predicts their needs, enabling banks to design more effective marketing campaigns.
- Churn prediction: AI identifies customers likely to leave and suggests retention strategies.
8. Cybersecurity
- Threat detection: AI systems monitor network behaviour and identify deviations from normal patterns, flagging potential threats such as malware, ransomware, or unauthorized access
- Automated threat response: AI can automate responses to threats, such as isolating affected systems, blocking IP addresses, or applying patches, reducing response time and limiting damage.
Challenges with AI
Despite the multiple ways how AI is disrupting the banking industry, its adoption is not without its challenges.
Some older, antiquated infrastructures will need radically updating to ensure AI solutions can be integrated, leading to potential concerns for ongoing service delivery while then systems are being upgraded.
Another area which is garnering criticism is because of ethical and bias concerns. It is possible for AI models to inherit biases from training data, leading to unfair outcomes in lending or credit assessments.
There are also concerns with data privacy and security. With increased data usage, ensuring compliance with regulations like GDPR and protecting customer information is critical.
Workforce impact has also been cited as being of concern. This is largely because automation may displace certain jobs, requiring banks to reskill or upskill employees for new roles alongside AI technologies. One way around this is to ensure close partnerships are maintained with the providers of AI platforms for banking to encourage the necessary reskilling and ongoing training.
Conclusion
AI is now firmly established as a main component of the modern banking and wider financial services sector.
It helps in multiple ways, ranging from helping customer service and personalization to credit scoring and lending. However, there are concerns – but these are already being worked on to ensure they do not present a fly in the ointment when it comes to major leaps forward that AI is presenting.
