The Evolution of Graph Database for Financial Institutions

Connecting the dots to reduce financial fraud and improve regulatory compliance

In the digital banking and fintech sectors, Indonesian institutions are witnessing remarkable expansion. This mushrooming complements Indonesia's national strategy for AI growth. Articulated in a blueprint, it will guide the country's AI development between 2020 and 2045 in areas like law enforcement, banking and health care.

Currently, customers are required to go through a computerised Know Your Customer (KYC) or third-party onboarding. To further simplify online banking operations and cope with demand, financial institutions will prioritise resources to focus on consumer and digital platforms. This paves the way for advancements in AI platforms such as Graph Database technology.

Without a doubt, Graph Database technology will be at the forefront of battling and preventing escalating online fraud, as well as minimising reputational risk and managing financial risk for financial institutions. By deploying graph database capabilities, institutions can prevent and detect financial wrongdoing such as money laundering and counter-terrorism financing.

Detecting and eliminating financial fraud with Graph Database technology has been eye-opening for financial institutions all over the world since this technology can detect fraudulent practices in real-time and allows for transaction tracking and quick human proofing. As a result, mismanagement and misappropriation of funds will be considerably reduced.

The use of Graph Database will be the most effective way to increase compliance and provide a sense of security whereby risk will be effectively mitigated, and any underwriting or loss of reputation can be avoided.

Financial Institutions transitioning to a Graph Database technology

Converting and connecting data into a usable graph schema has become commonplace thanks to the growth of Graph Database technology and the simplicity of learning and application.

Fast screening to catch any financial fraud

A graph model greatly improves decision-making and fraud detection processes in finance operations. Graph Database tech solves problems that are both impractical and practical for relational queries. This tool speeds up reviewing information based on the algorithm written and quickly provides evidence and alternative solutions to make a decision quickly and effectively.

Advantage of Real-Time information at scale for quick response 

To combat unethical behaviour, Graph Database provides quick modelling and advanced Machine Learning methods that lead to better detection due to the interconnected data. Real-time graph analytics can help explore, identify and forecast complicated relationships to make more accurate judgments. 

Exposure to the convenience of Multidimensional representation 

Financial Institutions can opt to track location and time using the Graph Database platform. This allows practitioners to weight edges to explicitly link entities that are close in location or time, which records time series data for fraud detection and raises the red flag.

OpenGov Asia is pleased to invite you to an exclusive OpenGovLive! Virtual Breakfast Insight  that aims to provide the latest information on delivering an effective method to detail evidence and practical decision-making using Graph Database. This is a closed-door, invitation-only, interactive session with top Indonesian financial institutions.  

OpenGovLive! Virtual Breakfast Insights are concise, to the point, strategic-level discussions designed to bring learning to the highest level! The unique proposition of an OpenGov Breakfast Insight is the integration of cutting-edge insights from our expert speakers and interactive discussion among the participants. 

We will discuss:

• Advantages in having concurrent querying and data updates in real-time for decision making
• Understanding how to use graph algorithms in conjunction with rapid analytics
• Cutting-edge Graph Database technology combined with Artificial Intelligence and Machine Learning Technology to smoothen data processing quickly
• Current strategies used to gather data, organising it into types and relationships to track and detect unethical acts with evidence
• Methods for mastering the power of graph relationships and deep analytics for speedy decision-making and accurate forecasting
• Advanced techniques for incorporating graph database platforms into finance analytics and Artificial Intelligence projects

Who should attend:

• Chief Innovation Officers  
• Chief Technology Officers 
• Chief Data Officers 
• Chief Analytic Officers 
• Chief Information Security Officers 
• Chief Strategy Officers 
• Heads of Fraud Strategy   
• Heads of Fraud Risk Modelling 
• Heads of Data  
• Heads of Digital Transformation  
• Heads of Analytics 
• Heads of Data Science