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KUALA LUMPUR, Malaysia, 26 June 2026 – Sunline positioned intelligent banking as an ecosystem-led transformation agenda at the China AI & Fintech Summit Malaysia 2026, also known as CAF Summit Malaysia 2026, bringing together banking leaders, China-based technology providers and local integration experts to examine how Malaysia’s financial institutions can build the next generation of intelligent, resilient and data-driven banking.

Held in Kuala Lumpur under the theme “Building Intelligent Banking Platforms: Core. Data. AI.”, the by-invitation-only executive summit addressed a critical question facing banks today: whether existing technology architectures are sufficiently agile, resilient and governed to support the next phase of AI-enabled banking.

As financial institutions across Southeast Asia move from digitalisation to AI-enabled operating models, the industry conversation is shifting from experimentation to production-scale readiness. For banks, the challenge has moved beyond access to AI tools. The greater priority is whether their core platforms, data architecture, infrastructure resilience and governance controls are mature enough to support real-time, compliant and scalable execution.

Against this backdrop, CAF Summit Malaysia 2026 connected China’s banking technology ecosystem with Malaysia’s local delivery environment. The summit brought together complementary perspectives across core banking, distributed data infrastructure, enterprise analytics, observability, infrastructure modernisation and implementation support, reflecting the increasingly integrated nature of banking transformation.

For Sunline, this approach reflects a partnership-led model for regional market development. Sunline contributes banking domain expertise, core modernisation experience and regional financial services engagement, while its ecosystem partners bring specialised capabilities across adjacent technology areas. The objective is not to present individual systems in isolation, but to show how different strengths can support a more coherent operating blueprint for financial institutions.

The key areas highlighted at the summit included:

• Core platform modernization: Sunline anchors the banking application layer, helping institutions move towards more agile, scalable and integration-ready core platforms.

• Enterprise intelligence: FanRuan connects banking data with BI and AI-enabled analytics to support reporting, decision-making and business insight.

• Distributed data infrastructure: OceanBase supports high-availability, real-time and large-scale database requirements for mission-critical banking workloads.

• Operational resilience: Cloudwise addresses the observability and response capabilities required in increasingly distributed technology environments.

• Infrastructure readiness: Huawei brings perspectives on mainframe core modernisation, cloud capability, and AI-ready infrastructure for financial services.

• Local execution: Cloudpoint Technology Berhad provides Malaysia-based integration, regulatory understanding, and service continuity to translate technology propositions into implementation.

Taken together, these areas reflect the breadth of Sunline’s partnership network rather than a single packaged solution. The value lies in connecting banks with relevant technology perspectives across core systems, data architecture, enterprise intelligence, operational control, infrastructure modernisation and local delivery. For Sunline, this reinforces its role as an ecosystem connector, helping align China-developed financial technology capabilities with the priorities and delivery realities of the Malaysian banking market.

The summit opened with remarks by Jack Wang, Managing Director, Sunline Malaysia, who highlighted the importance of bridging China’s fintech innovation ecosystem with Malaysia’s banking sector. His remarks set the tone for a programme focused on the balance between innovation ambition and execution discipline.

The summit gathered over 80 banking leaders, technology executives and ecosystem partners, reflecting strong industry interest in how Malaysia’s financial institutions can move beyond digital initiatives towards more scalable and resilient operating models. Key areas of exchange included core banking modernisation, trusted data quality, AI-driven observability, next-generation distributed databases, BI and AI-enabled data value, mainframe core modernisation and local execution.

A clear message emerged from the summit: AI adoption will not be defined by models alone. Sustainable value will depend on the readiness of core platforms, the quality of enterprise data, the resilience of technology operations and the ability of partners to translate capability into execution.

Sunline extends its appreciation to all participating banks, speakers, ecosystem partners and guests for contributing to CAF Summit Malaysia 2026. The participation of OceanBase, FanRuan Software, Cloudwise, Huawei and Cloudpoint Technology Berhad helped shape a focused industry exchange on how Malaysia’s banking sector can approach modernisation with greater clarity, resilience and delivery confidence.

CAF Summit Malaysia 2026 forms part of Sunline’s ongoing regional engagement with banks and technology partners across Southeast Asia. Through collaboration with China-based technology providers and local integration partners, Sunline continues to support industry dialogue and practical execution around core banking modernisation, data-driven transformation and AI-ready banking platforms.

About Sunline

Sunline is a banking software and technology services provider serving financial institutions across core banking, digital banking, big data, financial management and intelligent finance domains. With experience in financial technology innovation and implementation, Sunline works with banks and financial institutions to support digital transformation, platform modernisation, and data-driven business development.

As banks move from experimentation with generative AI towards production use, the challenge is shifting from what AI can generate to whether it can operate reliably within enterprise data environments. For financial institutions, an AI response is only useful when it reflects the right business definitions, follows governed processes, and can be traced back to an auditable execution path.

Sunline’s DataMind data intelligence platform addresses this challenge by combining semantic modelling with an enterprise Agent framework, embedding Agent capabilities directly into the platform foundation rather than treating them as standalone tools. The approach is designed to connect business context, data assets, and execution, eliminating hallucinations in large language models (LLMs) and creating a more controlled foundation for AI-enabled data operations.

From Data Storage to Domain Knowledge

A common limitation in data lakehouse environments is that models often describe physical tables rather than business meaning. Metrics, dimensions, and entities may exist in technical structures, but the business logic connecting them is not always explicit. This creates ambiguity when AI systems interpret financial concepts or generate queries.

• Entity-Centric Semantic Layer: DataMind constructs multi-dimensional knowledge graphs centered on core financial entities. By mapping business terminology (such as net interest margin or non-performing loan ratios) directly to physical data execution logic, the platform provides explicit context to the underlying models. This structural alignment eliminates derivation ambiguities and ensures explainable output for every query.

• Bridging the Knowledge Gap: DataMind addresses model hallucinations at the architectural level by combining a domain-specific semantic layer with deterministic execution protocols. By mapping business terminology directly to physical data execution logic, the platform provides explicit context to the underlying models, eliminates derivation ambiguities, and ensures explainable output for every query.

From AI Assistance to Governed Execution

Semantic context addresses what AI needs to understand; the Agent framework addresses how tasks are executed.

DataMind incorporates six core architecture features for enterprise AI operations, advancing the platform from a conversational tool into an enterprise-grade intelligent workbench:

• Multi-Tenant Governance: Centralized management of agent assets, knowledge bases, and scheduled workflows across enterprise teams throughout the asset lifecycle.

• Two-Tier Knowledge Base and Intelligent Retrieval Architecture: Connector-driven integration with existing IT assets utilizing the two-tier architecture to balance knowledge sharing with strict data isolation, while dual-path retrieval ensures the accuracy of financial-grade Q&A. 

• Plan-Based Execution Framework: Complex tasks are decomposed into defined steps, checked before progression, and recorded for audit purposes. This approach is intended to make AI behavior more deterministic and suitable for production environments.

• Standardized Capability Packaging: DataMind supports modular packaging of Agent capabilities, enabling standardized circulation and dynamic loading of Agent assets. 

• Seamless Resource Integration: Support from Agent-Mode-Package specification and the Model Context Protocol (MCP) enables agents to connect with external tools and resources in a standardised way, supporting broader integration across the banking technology infrastructure.

• Secure Sandbox Runtime Environment: Execution of sensitive data tools within MicroVM sandbox environments, ensuring isolated, policy-compliant execution boundaries.

Moving Data Operations Towards Automation

Deployed within multiple banking environments, DataMind demonstrates measurable productivity gains across three core operational scenarios:

• DataOps Delivery: DataMind establishes a collaborative mechanism consisting of a Master Control Agent, five categories of Agents covering offline development, real-time development, batch scheduling, data services, and intelligent operations, and an Audit Agent. This mechanism automates an end-to-end closed-loop pipeline creation from natural-language requirements to production deliverables, achieving a 70% increase in data engineering efficiency.

• Self-Service Data Access: Semantic modelling supports the process from metric interpretation to SQL generation and visualization, with a reported 80% improvement in self-service data access efficiency. 

• Automated Analytics & Insights: Agents support tasks such as metric fluctuation attribution and DuPont analysis, generating visual reports and accelerating executive insight generation by 90%.

These results point to a broader shift in enterprise data operations: AI is moving beyond an interface for individual tasks and becoming part of the underlying development and execution workflow.

Building a More Governed Foundation for AI in Banking

By integrating semantic modeling with enterprise agent orchestration, Sunline DataMind provides a deterministic bridge between complex raw data assets and strategic decision-making, delivering operational resilience, strict regulatory alignment, and verifiable engineering efficiency. 

As banks progress towards more AI-enabled operating models, these foundations will become increasingly important in determining whether AI can move from individual productivity use cases into repeatable, enterprise-scale data operations.

Shanghai, China, May 19, 2026 – Under the theme “Building Intelligent Banking Platforms”, the Sunline Banking Summit Shanghai 2026 took place in Shanghai Zhujiajiao, gathering senior financial leaders, technology executives, ecosystem partners and industry experts from the Middle East, Central Asia, Singapore, Malaysia, Indonesia, Thailand, Vietnam, Hong Kong and other key markets for cross-regional dialogue on the next phase of intelligent banking.

Returning to Shanghai for its second year, the gathering underscored Sunline’s growing role as a regional platform where banking leaders and ecosystem partners examine the next stage of technology-led transformation. As banks continue to modernise mission-critical systems, strengthen data foundations and explore the practical adoption of AI, the focus is shifting from isolated digital initiatives to integrated platforms that can support real-time operations, trusted decision-making and long-term resilience.

Centred on the architecture of intelligent banking, Sunline articulated a platform vision where modern core systems, AI-ready data foundations and governed execution capabilities form the backbone of a more connected financial institution. Its capabilities across future-proof core banking architecture, intelligent automation, trusted AI adoption, data quality management, integrated loan lifecycle management and mainframe offloading reflected how banks can move beyond fragmented transformation efforts towards operating models built for resilience, intelligence and scale.

“Banks have reached a critical point where incremental technology additions can no longer address the structural limitations of legacy systems,” said Ruby Zhou, Deputy President of Sunline International. “The future of banking will be shaped not by the volume of digital tools deployed, but by the strength of the architecture that connects them. Through core modernisation and AI-ready data foundations, we believe banks can move beyond technical debt and build the resilience, intelligence and agility required for the next era of finance.”

As the banking industry moves into a more intelligent and architecture-led era, the ability to connect core systems, data foundations and AI capabilities will increasingly define institutional competitiveness. Sunline will continue to work with financial institutions and ecosystem partners to shape this next phase of banking transformation, helping banks build the resilience, intelligence and execution capacity required for the future of finance.

Bangkok, 8 May 2026 – At the Huawei Thailand Partner Summit 2026 in Bangkok, Sunline joined more than 400 partners from across key industries under the theme “Together, Advancing Industrial All Intelligence”, as ecosystem collaboration continues to advance intelligent transformation and AI-enabled innovation across industries.

At the “RONGHAI Solution Salon” for the financial industry, Sutee, Country Head of Sunline Thailand, highlighted a recent collaboration between Sunline and Huawei on a leading banking project in Asia, emphasizing how greater convergence across core banking platforms, infrastructure architecture, and ecosystem capabilities can support financial institutions in building scalable, resilient, and future-ready operating environments.

The collaboration brings together Sunline’s core banking expertise and Huawei’s digital infrastructure capabilities under the Huawei RONGHAI Financial Partner Program. By leveraging the “4-Win” collaboration model, this partnership supports large-scale banking transformation initiatives through cloud-native architecture, integrated data capabilities, infrastructure optimisation, and ongoing co-innovation across the financial services ecosystem.

As intelligent transformation continues to reshape the financial sector, closer collaboration —driven by the RONGHAI initiative—among financial institutions, technology providers, ISVs, and ecosystem partners is becoming increasingly important. By deepening the “4-Win” collaboration model, we aim to strengthenoperational resilience, accelerating technology modernisation, and supporting sustainable industry development.

Through its continued participation in Huawei’s Global Financial Partner RONGHAI Programme, Sunline remains committed to deepening strategic collaboration with Huawei, financial institutions, SIs, ISVs, and ecosystem partners to accelerate modernisation initiatives and deliver long-term value in the digital and intelligent era.

Demonstrating the power of replacing legacy frameworks with flexible, high-performance execution layers, Sunline recently achieved a perfect score across performance, architectural integrity, and full lifecycle coverage during a rigorous Proof of Concept (POC) evaluation for a major joint-stock commercial bank. By replacing rigid monolithic structures with modern platform architecture, institutions can seamlessly manage peak digital demand across modern lending models while unlocking the building-block agility required for continuous product innovation. 

Solving the “Scale Paradox” with Sunline’s Execution Layer

The global banking industry is entering a decisive phase of digital transformation. As customer expectations shift toward real-time, personalized credit experiences, institutions are under pressure to modernize legacy lending platforms that were never designed for scale, agility, or ecosystem collaboration. This tension between traditional infrastructure and the demands of digital-first lending has become a defining challenge for retail banks worldwide.

Modern retail lending-spanning proprietary digital loans, co-lending models, and credit-as-a-service-requires an infrastructure that can handle extreme volatility in transaction volumes while maintaining rigorous risk controls. Many established institutions face a critical bottleneck: the Scale Paradox. Traditional lending infrastructure was built for predictable, batch-driven operations, not for extreme transaction volatility or ecosystem collaboration. For this specific institution, the objective was clear: replace a restrictive legacy framework with a high-performance digital lending foundation capable of supporting sustainable, large-scale growth.

A Platform-Based Approach: A Microservices-Led Execution Layer

Sunline’s engagement centers on building a platform architecture that supports the full lifecycle of digital lending. Moving away from monolithic structures, the new architecture utilizes a microservices and component-based design. This allows the bank to manage the entire credit lifecycle-from automated onboarding and limit management to real-time decisioning and automated accounting-as a series of orchestrated services.

Key to this methodology is the implementation of a robust execution layer. By leveraging an advanced gPaaS (General Platform as a Service) framework and high-performance batch and online processing engines, the platform functions with “building block” flexibility that allows a bank to iterate on new products as quickly as the market demands. This ensures that the technology stack remains a driver of revenue growth, rather than a bottleneck to innovation.

Validating Performance at Scale

During the rigorous POC phase, Sunline’s platform was subjected to high-concurrency stress testing to simulate the demands of modern digital traffic. The results demonstrated a system throughput and response latency that exceeded global benchmarks, confirming the platform’s ability to maintain stability during massive transaction peaks. Beyond raw performance, the evaluation validated the platform’s data migration integrity and its business semantic model, ensuring that complex credit data remains consistent across the enterprise during the transition.

The Strategic Impact: Toward Intelligent Operations

The deeper integration between Sunline and the bank underscores a wider industry trajectory: digital lending is evolving into a platform capability that connects banks, fintechs, and partners through shared data and execution layers. By aligning the lending execution layer with the bank’s core transformation, the institution achieves a higher level of operational intelligence and data-driven decisioning.

Sunline remains committed to driving this industry shift. We are not just positioning ourselves as the center of the ecosystem but as an enabler within it, helping banks build the digital foundations required for lending in an era defined by agility, resilience, and collaboration. In the years ahead, such platform-based approaches will shape how retail credit is delivered: more efficient, more inclusive, and more aligned with the expectations of a digital-first economy.

At Huawei Global Financial EcoWeek 2026, where ecosystem leaders from across the global financial services and technology landscape gathered to examine the next phase of financial transformation, Sunline was featured as a key core banking partner of Huawei. Discussions centred on how banks today are responding to rising operational complexity, increasing data demands, and the need for more agile, data-led operating models.

With twenty-four years of experience in financial technology and an established eight-year strategic collaboration with Huawei, Sunline drew on its track record of delivering large-scale transformation programmes, including a recent implementation for a leading bank in Singapore. As one of the largest banks in Asia by assets, it is consistently recognised among the world’s best banks. These experiences reflect a growing shift towards platform-based transformation in complex and evolving banking environments.

“Sunline’s partnership with Huawei is grounded in long-term alignment across both architecture and execution. By combining core banking capabilities with a robust infrastructure foundation, banks can modernise with greater certainty while retaining the flexibility to evolve. Through this collaboration, structured and risk-managed transformation approaches have been developed to address key operational and architectural challenges, enabling more scalable and resilient operating models,” said Jack Wang, Managing Director of Sunline International.

Data Governance as a Foundation for Scalable and Resilient Banking

As discussed during the Data Intelligence roundtable session, regional implementation experience shows that a standardized and consolidated single source of trusted master data is the foundation for reliable data analysis and AI use cases across intelligent marketing, risk management, operations, and data visualization. Especially when designing a 3-to-5-year data transformation journey, strong data governance empowers more effective data analysis for future business cases. Supported by DataMind, Sunline’s data intelligence platform, this approach spans data standards, quality management, metadata governance, and lifecycle oversight, enabling more consistent and dependable data usage across enterprise functions.

DataMind establishes a governed data layer between infrastructure and application layers, supporting consistent data flow across both underlying systems and business-facing platforms. This reduces fragmentation, improves cloud integration, and provides a more reliable foundation for operational processes and downstream use cases.

Integration of Core Systems and Data Architecture

Through its collaboration with Huawei, Sunline extends this capability by aligning core banking systems with real-time data platforms within a unified, platform-based architecture.

This alignment enables transactional processing and data management to operate in coordination, strengthening operational resilience and improving consistency across business functions. It also supports a more responsive operating environment, where decision-making is increasingly driven by timely and reliable data.

As financial institutions modernise their technology environments, core systems, data foundations and infrastructure are increasingly integrated within cohesive architectures. Sunline’s strategic direction, together with ecosystem partners such as Huawei, reflects this shift, supporting more resilient and future-ready operating models across the global banking landscape.

At the Huawei China Partner Conference 2026, Sunline officially launched the joint “Semantic Data Layer + Agent” Full-Stack Intelligent Marketing Solution with Huawei. The initiative marks a strategic shift for financial institutions, moving from passive “massive data storage” to “proactive intelligent decision-making.”

The solution integrates the core “Semantic Data Layer + Agent” architecture of Sunline’s DataMind platform with Huawei’s robust infrastructure, including MRS services and DWS databases. Sun Shisheng, Executive Assistant to the Group CEO of Sunline Group, Sunline Group attended the signing ceremony along with Huawei representatives.

Bridging the Gap to “Intelligent Computing”

During a keynote session, Celia Zhou, Solution Director at Sunline, introduced the scheme’s multi~agent collaboration and various application scenarios, highlighting that the era of AI demands a transition from traditional lakehouse architectures to a centralized intelligent computing hub. This new paradigm addresses critical industry pain points such as fragmented customer data, prolonged model deployment cycles, and product homogenization and need misalignment caused by a lack of customer insights.

Transforming Data into Growth

By utilizing Sunline’sDataMind semantic data modeling and multi~agent applications, the solution transforms fragmented data into financial data model, a structured data with clear business semantics. This provides the logical foundation for five specialized AI agents:Model, Mapping, Plan, Simulate, and Report Agents. Together, they automate the banking marketing lifecycle, from capturing customer intent in real-time to executing autonomous, precision-targeted actions.

Proven Business Impact

The solution has already demonstrated significant ROI in retail wealth management scenarios.Key performance highlights include:

• Campaign Agility: Launch cycles reduced from days to under 2 hours.

• Engineering Efficiency: 80% reduction in repetitive SQL development.

• Market Insight:50% increase in customer persona coverage.

Foundation of Innovation: Sunline’s APStack & Kunpeng Integration

In a private session on infrastructure innovation, Sun Shisheng shared Sunline’s success in core production system renovation. He highlighted how the APStack technology platform, natively developed on the Huawei Kunpeng ecosystem, powers core banking, enterprise ledgers, and data asset systems. Sun emphasized that Sunline will continue to innovate alongside Huawei Kunpeng to inject new momentum into the industry’s digital transformation. Looking ahead, Sunline and Huawei remain committed to deepening their R&D collaboration, exploring new AI-driven frontiers to turn data assets into a sustainable engine for financial growth.

SHENZHEN, March 20, 2026 — At the Huawei China Partner Conference 2026, Sunline was awarded two top honors: the Joint Solution Excellence Award and the Joint Solution Incubation Award. Staged under the theme “Empowering Synergy, Achieving Excellence,” the conference convened key technology providers shaping enterprise digital infrastructure. For Sunline, securing dual honors from Huawei represents an important industry signal, validating its architectural positioning in distributed core banking and cloud-native software engineering.

Global Core Modernization and Strategic Alignment 

By aligning the core banking solutions with Huawei Cloud and the Kunpeng computing architecture,Sunline has engineered a fully integrated, distributed core banking stack. This joint architecture addresses several persistent engineering challenges in core modernization:

• Domain-Driven Application Planning: Utilizes enterprise-grade business modeling to decouple core ledger functions from peripheral services, allowing banks to launch new financial products without altering base accounting logic.

• Unitized Architecture and Operational Resilience: Employs a cell-based deployment model designed for multi-active, zero-data-loss disaster recovery. This framework ensures continuous availability and high-throughput transaction processing during peak traffic spikes.

• Infrastructure-Level Optimization: Tunes database interactions and transaction processing logic against underlying processor microarchitectures, yielding lower latency and improved compute utilization.

Advancing Distributed Core Architecture 

At the event’s Digital & Intelligent Finance Exhibition, Sunline showcased its Distributed Core Banking Solution – SunCBS. Built on a cloud-native foundation, the solution is designed to facilitate core system modernization through:

• Enterprise Business Modeling: Streamlining complex banking operations.

• Unitized Architecture: Supporting massive horizontal scalability.

• Zero-Loss Disaster Recovery: Ensuring high availability and data integrity.

The solution has been implemented across multiple commercial banking tiers, including national joint-stock banks and regional financial institutions. These live deployments demonstrate that the combined stack meets strict regulatory compliance requirements, supports real-time transaction processing, and offers global banks a validated, cloud-native foundation for digital transformation and long-term technology sovereign resilience.

As Generative AI accelerates across the financial sector, the limitations of traditional data models are becoming dangerously apparent to safety and compliance risk. Built as static structures for human analysts, the legacy data models lack the dynamic business semantics that AI requires. This semantic gap leaves AI struggling to understand true business context, leading to reasoning errors, AI hallucinations, and severe compliance risks.

To solve this critical industry bottleneck, Sunline has announced a major architectural upgrade to its DataMind platform. By transitioning data management from “static reporting” to “cognitive-driven” intelligence, DataMind bridges the gap between raw data and business logic using Ontology and Multi-Agent System.

The Evolution of the DataMind Architecture

Traditional lakehouse architectures face three major pain points in the AI era: semantic gaps, data hallucinations, and permission black boxes. To overcome this, DataMind introduces a dual-engine architecture: physical Lakehouse Storage Layer paired with a Cognitive Semantic Layer (Ontology).

This approach breaks down the boundaries of traditional data processing. It transforms massive data sets into living knowledge embedded with native business logic. AI is no longer just retrieving data; it is understanding relationships, grasping business fundamentals, and outputting highly trustworthy results, just like a senior business expert working on duty.

DataMind’s Dual-Engine Architecture

A new standard is emerging as data management shifts from isolated silos to human-AI collaboration. The focus is no longer just asking, “Which table is this data stored in?” Instead, the new approach asks: “What does this data mean to the business, and how is it connected to everything else?” This creates a web of business relationships that machines can easily understand, marking a massive leap from flat, two-dimensional data storage to a multi-dimensional, intelligent understanding of your business.

DataMind turns this concept into reality through a proven six-step workflow: 1. Requirement Analysis → 2. Ontology Design → 3. Data Mapping → 4. Data Exploration → 5. Action Development → 6. Business Review. Supported by Large Language Models (LLMs), it seamlessly transforms abstract business concepts into executable data models.

By using this intelligent semantic layer as its core engine, DataMind delivers value to banks in three key ways:

• Injecting business common sense into AI: By explicitly defining the semantic relationships between “Customer → Product → Event,” DataMind fundamentally mitigates Large Language Model (LLM) hallucinations.

•  Conversation as development: Business users can now use natural language to describe requirements, with the system automatically generating rules and execution scripts.

•  Building core cognitive assets: As the business evolves, these semantic models accumulate, becoming irreplaceable, proprietary digital assets for the bank.

The Intelligent Agent Ecosystem

In real-world execution, DataMind leverages a collaborative network of intelligent agents to build a complete, end-to-end data modeling pipeline:

•  Intelligent Modeling (Model Agent): Users can now describe their goals using large language. For example: “Build a customer-product model for retail insurance and include risk attributes.” The Model Agent instantly understands the request, applies built-in business logic, and generates a complete semantic model (previewed in a familiar Excel-like interface). Once approved, the model is published instantly, compressing a process that used to take days down to just minutes.

•  Automated Rule Generation (Mapping Agent): When building reports, users simply describe their desired table structure or upload a physical model file. The Mapping Agent automatically analyzes the relationships and generates ready-to-use mapping rules and ETL scripts. This creates a seamless pipeline from the underlying data layer straight to your business applications.

Proving the Value: Re-inventing Precision Marketing

The true test of any architectural upgrade is the ROI it delivers in the real world. Traditionally, launching a bank’s precision marketing campaign is a clunky, cross-departmental relay race. It takes 3 to 5 days of manual audience filtering, rule configuration, and performance analysis. DataMind changes this entirely. By combining its core data models with a five-agent ecosystem (Model, Mapping, Plan, Simulate, and Report Agents), the platform transforms “blind outreach” into an intelligent, closed-loop marketing engine.

In retail wealth management scenarios, the business impact is massive:

•  Faster Time-to-Market: Campaign launch cycles are slashed from 5 days down to just 2 hours.

•  Smarter Targeting: Customer profile coverage is increased by 50%.

•  Reduced IT Bottlenecks: Repetitive SQL coding is reduced by 80%.

Most importantly, business operators no longer need to wait on IT to pull reports, they can simply “chat” with their data to make immediate, informed decisions.

The Path to an AI-First Future

From architectural evolution to proven market execution, Sunline continues to pioneer technology that answers the demands of the AI era. As the financial sector accelerates its digital transformation, DataMind equips banks to finally break down the barriers between raw data and business logic. It empowers financial institutions to move beyond experimental “+AI” projects and transition into a truly strategic, “AI-First” enterprise.