McKinsey estimates that generative AI could add up to US$340 billion ($456 billion) in value annually to banks. To unlock this value, banks need enterprise search capability to perform Google-like searches to easily find information within the organisation. So, why is this important?
Generative AI models require vast amounts of data for training and operation and enterprise search ensures these models have access to high-quality, relevant data. “For generative AI to work, you need a search engine to bridge the gap between information in the public domain and internal private data that is rapidly changing,” Ravi Rajendran, area vice president of Asean and Greater China at Elastic, tells DigitalEdge.
While generative AI can interpret and generate content, enterprise search can provide contextual data that improves the AI’s accuracy and relevance. Combining search with generative AI allows banks to derive deeper insights from data, enabling predictive analytics and proactive decision-making.
For example, Rajendran says a generative AI model can draft customer communications based on insights derived from enterprise search, ensuring the messages are relevant and personalised.
Addressing critical challenges
Beyond generative AI, enterprise search can handle massive data sets in near real-time and address the challenge of data silos. For instance, Bank Rakyat Indonesia needed complete visibility into its sprawling IT infrastructure, compounded by increasing data volumes and cyber threats. Using Elastic’s search and observability solutions, the bank overcame data silos, streamlined troubleshooting, improved resource allocation and adopted a proactive IT management approach. This allowed the bank to seamlessly integrate its infrastructure with hybrid and multi-cloud deployments, future-proofing operations.
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Enterprise search can also help banks better prevent fraud and comply with regulations. For instance, the Monetary Authority of Singapore mandates that banks report any suspicious activities or incidents of fraud within five working days of discovery, especially if these issues impact the bank’s safety, soundness or reputation. “To meet this criteria, banks can set up filters and rules on search tools which trigger alerts when specific criteria are met, allowing for accelerated threat detection,” says Rajendran.
“For anti-money laundering, enterprise search helps in the swift identification of suspicious transactions by cross-referencing data across different systems. In customer segmentation, banks can leverage enterprise search to analyse customer data, enabling precise segmentation and personalised marketing and facilitating more effective customer due diligence and know-your-customer processes.”
Misconceptions about enterprise search
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Rajendran says there is an increased interest in enterprise search solutions among banks in Asia Pacific as they seek to become data-driven while complying with regulations. He adds: “Stringent regulations across Apac necessitate robust data management and searchability. Enterprise search can streamline compliance efforts by enabling efficient data retrieval and analysis, helping banks find the relevant answers to comply quickly and efficiently.”
Some regional banks hesitate to adopt enterprise search, mistakenly believing it is complex, costly and only beneficial for large institutions. Additionally, cybersecurity concerns arise due to the vast amount of sensitive information banks hold, such as financial records, account details and personal identifiers.
“Modern enterprise search solutions, like that offered by Elastic, provide user-friendly interfaces and require minimal technical expertise to deploy and maintain. They also come with strong security protocols to protect banks’ sensitive information,” says Rajendran.
Getting the most value
To benefit from enterprise search, Rajendran advises banks to look for a scalable and flexible platform with advanced filtering capabilities to ingest data from various sources, such as databases, file systems, and cloud applications. “It is also important to regularly analyse search logs and user behaviour to identify areas for improvement and refine the search experience. By doing so, different users with different needs will benefit from the technology, accelerating decision-making,” he adds.
An effective enterprise search solution enables banks to address new and amended regulations continuously. An innovative and scalable platform, such as the Elastic Search AI platform with strong observability features, will allow banks to easily locate relevant documents and reports for audits or investigations and demonstrate a clear audit trail for regulatory agencies.
Rajendran says the solution should prioritise searchability and integrate with security information and event management tools, providing a centralised view of user activity and data access. He adds: "This combined observability and search AI capabilities will empower banks to ensure regulatory compliance and simplify the audit process alongside investing in comprehensive training programmes to ensure users are proficient in leveraging enterprise search technology.”
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Banks must also ensure their enterprise search platform adheres to strict data security regulations and prioritises security with features like role-based access control and encryption to safeguard sensitive customer information. For instance, the Elastic Security solution can help enhance a bank’s cybersecurity posture. It features Elastic's Attack Discovery, which utilises large language models to automate threat detection and investigation.
“By correlating disparate events, Elastic's Attack Discovery provides a holistic view of potential threats and identifies subtle patterns missed by traditional methods, leading to earlier detection and prevention of sophisticated attacks,” adds Rajendran.
Given the wide range of enterprise search solutions available, Rajendran highlights the importance of choosing a solutions provider with a strong understanding of the financial services industry and Apac regulations. “Banks should focus on features and not just outcomes. Instead of simply seeking “better search”, bank leaders like chief technology officers must first clearly define success metrics. Aligning the solution with specific needs, like improved loan processing times or enhanced customer service, leads to a more successful implementation,” he continues.
Understanding that data is the crown jewel for banks, enterprise search can unlock the value of data trapped in disparate systems. This capability accelerates decision-making, mitigates risks and ultimately boosts the bottom line.
