By Richard Scott, Group Vice President Asia Pacific, Informatica
Australian banks have experienced a challenging year due to global instability in the financial services sector and the nation’s cybersecurity and Privacy Act reforms. These factors are adding pressure on banks in terms of data management and usage.
Furthermore, the rise of the digital economy has transformed the banking customer, who is now more price-conscious and less loyal to specific brands. A recent Australian Banking Association Senate Inquiry submission revealed that almost 70% of banking customers are switching between lenders.
Consequently, banks must adapt to these changes and address the need for efficient data management in the era of generative-AI.
There is a growing divide in banking between the traditional coded systems and the low code/no code world. Coded systems are not compatible with generative-AI technologies, which demand strong data quality, governance, and cadence.
Many Australian banks still heavily rely on in-house coding, leading to siloed business units and data, which hinders their agility in the digital space.
The potential of generative-AI in banking
In banking and other sectors, generative-AI has the potential to significantly impact productivity, roles, staffing, and cost savings for data management. This includes leveraging generative-AI and natural language processing to expedite data search, autonomous data matching and merging, building data pipelines, and streamlining data quality processes and classification.
Utilizing generative-AI for customer engagement in banking
As the loyalty of Australian banking customers is under pressure, there is a growing need for banks to adopt new business models that embrace generative-AI to enhance personalized customer engagement. The aim is to obtain a comprehensive view of each customer and deliver targeted customer engagement.
The critical need for business-ready data in generative-AI for banking
In order to gain a complete view of banking customers, data must be ‘fit for business use’, accessible, clean, valid, transparent, governed, and understood by all users. The cost of not having such data is customer attrition, as 15 out of every 100 customers leave their financial institution annually.
Why banks are investing in key areas
- Modernizing applications to unify clean, trustworthy data from customer-facing systems to internal systems.
- Operationalizing AI and machine learning in areas such as ChatGPT for enhanced data management tasks.
- Embracing open banking APIs to meet market and regulatory demands.
- Improving data literacy among executives and employees for effective data-driven decision-making while ensuring data privacy, security, and governance.
- Implementing centralized and controlled ESG reporting solutions for transparency and accountability.
Challenges faced by Australian banks
Australian banks continue to be hindered by legacy data management tools, lack of data literacy among banking executives and employees, and the proliferation of sensitive data. This has led to data sprawl, replicating data problems, and governance challenges.
The solution lies in technology-agnostic, cloud-native data management solutions that can seamlessly integrate with existing bank systems, provide secure data sharing, and enable actionable insights for better business decisions, risk mitigation, and enhanced customer experiences.
