Banks can benefit from artificial intelligence (AI), provided they can keep their data secure and trust what the AI tells them. AI has the potential to revolutionize the banking industry by streamlining processes, improving customer experience, and providing valuable insights.
The Data Deluge in Banking
Even a moderate-sized bank can have millions of customers and onboard hundreds of thousands of new customers yearly. Each customer generates hundreds of pages of documents:
- Account origination (Know Your Customer) requires ID documents.
- Loans and mortgages require bank statements, pay stubs, credit information, and credit card statements
- Non-customer documents include purchase orders, regulatory filings, contracts, invoices, etc.
Each document contains a wealth of information, such as gross salary, net salary, tax information, and benefits information from pay stubs. This data can provide valuable insights to the bank if the information can be extracted quickly and efficiently.
Document Management and Data Extraction
Each document contains a wealth of information - pay stubs, for example, include gross salary, net salary, tax information, benefits information, and so forth, all of which can provide valuable insights to the bank if the information can be extracted. To be useful, document evaluation needs to happen quickly, which means bank employees need to be able to find and access documents easily. Manually reviewing and approving (or denying) these documents requires dozens of people per department. The work is slow and error-prone.
AI-Driven Efficiency
AI can help. Banks, such as the Commonwealth Bank of Australia (CBA), are already using AI to extract information from documents and store it for later use. Banks are seeing significant gains in productivity from using AI - for example, AI enables banks to maintain a 360-degree view of each customer and keep that view up to date.
Challenges in Implementing AI in Banking
AI still has significant drawbacks. The data must be brought to the AI. Whether that means moving data to a vector database or the cloud, moving data generates significant costs and potentially compromises security. Data format conversions can cause information to be lost.
Data Retrieval Issues
Data retrieval is still a struggle. Employees frequently do not know what data they have or where to look, making finding data a chore that saps morale. Businesses are often locked into using a specific AI model from one vendor rather than being able to use the model that best fits a specific use case.
AI Model Reliability
And, of course, data returned by an AI model is subject to errors and hallucinations.
Despite the benefits, AI still has significant drawbacks in the banking industry. Summing up:
- Data Migration: The data must be brought to the AI, which can generate significant costs and compromise security. Data format conversions can cause information to be lost.
- Data Retrieval: Employees frequently do not know what data they have or where to look, making finding data a chore that saps morale.
- Vendor Lock-in: Businesses are often locked into using a specific AI model from one vendor rather than being able to use the model that best fits a specific use case.
- AI Errors and Hallucinations: Data returned by an AI model is subject to errors and hallucinations, which can lead to incorrect decisions and insights.
Overcoming Obstacles in Banking with SWIRL
Fortunately, SWIRL solves these problems. Swirl technology:
- Keeps data in place: SWIRL enables Generative AI to connect with source systems, bringing the AI to the data.
- Unifies search: SWIRL simultaneously searches all data and content repositories an employee can access.
- Adopts existing security: SWIRL utilizes the source system security policies, following a zero-trust security approach to ensure users access only what they’re allowed.
- Provides AI flexibility: SWIRL supports using multiple LLMs and AIs, including company-created models located behind the firewall.
- Is data format agnostic: SWIRL supports structured data from databases, as well as content from email, office documents, collaboration tools such as Teams and Slack, and more.
- Enables pilot to co-pilot: SWIRL enables simultaneous communication with multiple co-pilots.
- Returns results you can trust: SWIRL’s innovative use of Retrieval Augmented Generation grounds results in facts, reducing or eliminating the risk of AI hallucinations and errors.
Swirl technology enables banks to benefit from the latest AI advancements while maintaining data security and flexibility.
Contact us to learn how Swirl can help you improve your data analysis and retrieval.