Recently, Cloudera, alongside OCBC, were named winners in the“Best Big Data and Analytics Infrastructure Implementation” category at The Asian Banker’s Financial Technology Innovation Awards 2024. This recognition underscores the importance of trusted data when building AI and generative AI (GenAI) models and serves as a testament to the impact that reliable data can have in real world use cases.
As we celebrate this win, let’s explore the work that Cloudera and OCBC did together and why a trusted AI is so critical to effective AI.
The Role of AI in Banking
2024 continues to witness the rapid development of AI and its applications, with GenAI leading the charge. The McKinsey Global Institute estimates that across various industries worldwide, generative AI could contribute an annual value ranging from US$2.6 trillion to US$4.4 trillion. Banking, in particular, stands to gain significantly, with an estimated increase in revenue of US$200 billion to US$340 billion, equivalent to between 9% and 15% of their operating profits.
To keep pace as banking becomes increasingly digitized in Southeast Asia, OCBC was looking to utilize AI/ML to make more data-driven decisions to improve customer experience and mitigate risks.
Through Cloudera, OCBC built a data lake and an Enterprise Data Science platform in a private cloud environment to introduce a more resilient infrastructure and platform capable of managing projects with increasing volume, variety, and velocity of data, while also enabling real-time analytics. With Cloudera, OCBC’s Next Best Conversation platform was able to analyze real-time contextual data from customer conversations, resulting in a revenue increase.
While these are great proof points to demonstrate how business value can be driven by AI/ML, this was only made possible with trusted data.
Trusted Data is the Foundation of AI
According to a Cloudera survey, Data Architecture and Strategy in the AI Era, 57% of APAC organizations are at least early-stage adopters of AI. Gartner’s predictions also estimate 80% of enterprises will adopt GenAI APIs and models or deploy GenAI-enabled applications in production environments by 2028.
With adoption growing, organizations need to understand that without good quality and trusted data, there is no way to implement AI. Unreliable data leads to unreliable AI models, like building a house on a foundation of sand that might shift and collapse. On the other hand, trusted data is like building a house on solid concrete by ensuring AI models are trained on traceable and accurate information, enabling them to establish patterns and deliver dependable results. This translates into sharper insights for better decision-making capabilities and improved business outcomes.
Data biases are another element banks need to take into account, as data can unknowingly harbor biases which may be amplified and skew insights – like rejecting loans submitted by eligible applicants. Trusted data practices reduce this risk by ensuring data sets are well-balanced and representative, fostering fairer and more ethical AI implementations.
Lastly, data security is paramount, especially in the finance industry. Highest standards of data governance prioritize robust security features – such as storage encryption, data access authorisation, and data stewardship – as well as access controls to protect sensitive customer information, mitigating the risk of breaches from either external threats, or carelessness and negligence from internal stakeholders.
Smarter and Safer Banking for the Future
With the continuous evolution and adoption of AI and Generative AI (GenAI), the future of banking is rapidly transforming. Trusted data is paramount for banks to unlock the true potential of AI and ML, which will in turn provide personalized customer experiences, better fraud detection for enhanced security, and ultimately pave the way for a smarter and safer banking future for all.
Learn more about how Cloudera helped OCBC unlock business value with trusted data.
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