Document creation time
reduced by 99%
Average Labor Cost
Reduced by 70%
Average Work Speed
Improved by 30%
Data Modularization
OLA modularly manages various financial data sources, including internal documents, transaction data, and public disclosures. You can selectively connect only the necessary data or easily add new data sources. For example, you can integrate a bank's customer Q&A database, financial news, or disclosure APIs into OLA's content hub for the AI to utilize.
Application Modularization
It can be configured in various AI application forms, such as chatbots, automatic report generators, and search/analysis tools. On the OLA platform, you can turn on and off and combine necessary functions, such as chatbot modules, report generation modules, and notification modules, to support a variety of business scenarios with a single platform.
Various use cases of financial AI
Through the modular OLA platform, various types of financial AI services can be implemented.
Client
Type
Financial Chatbot
Usage
Financial chatbot inside the MTS application
OLA-based Financial Chatbot
We build AI chatbots that allow bank customers and employees to ask and answer questions through the website. For example, it improves work efficiency by automatically responding to inquiries about financial products and internal guidelines 24 hours a day.
Client
Type
Automated Report Generation
Usage
A solution where AI combines the data users want and then generates the desired reports.
Report Automation
We will implement an automatic report generation feature available on the mobile app. Instead of sales field staff writing customized proposals or financial reports themselves, they can simply enter the required information in the app, and OLA will automatically complete it for them.
Client
Type
Automated Report Generation
Usage
A solution where AI combines the data users want and then generates the desired reports.
An electronic disclosure analysis engine that anticipates lock-up releases and supply-demand shifts
We collect DART real-time disclosure metadata, extract HTML for each disclosure table of contents in parallel, and then use LLM to filter only tables related to the float/tradable volume and convert them into structured data.
It supports fast structuring of disclosures that affect supply and demand, such as capital increases, capital reductions, mergers, and spin-offs, as well as calculating lock-up release dates based on securities registration statements (equity securities), so they can be immediately checked on the dashboard.
Client
Type
Internal Network Disclosure Chatbot
Usage
An intranet chatbot that quickly finds and allows inquiries about the numerous public disclosures posted daily, while also providing summaries and analyses of those disclosures.
Internal Network Disclosure Chatbot
We will implement a public disclosure information query chatbot for use within the internal private network. To help financial company employees quickly find the latest disclosure or financial statement information, OLA learns vast amounts of disclosure data and provides summaries/analyses in response to inquiries.
Step 1. Diagnosis
Understand existing business operation methods
Identify data sources and security policies
Plan new services
Step 2. PoC
Understand existing business operation methods
Identify data sources and security policies
Plan new services
Step 3. Official Adoption
Understand existing business operation methods
Identify data sources and security policies
Plan new services
Step 4. Final Testing and Delivery
Conduct final QA
Write service manual
In-house demonstration and training if necessary