Bank of Singapore and Hong Leong Bank Adopt Arta AI to Boost Advisory Productivity
Two major banks have plugged Arta AI into their wealth platforms to sharpen advice, speed up research, and deliver more precise portfolio decisions. Bank of Singapore and Hong Leong Bank are using the SaaS platform to connect client portfolios, firm data, research, and product intelligence so their teams can work faster and with greater confidence.
For leaders, this is less about hype and more about throughput, quality of recommendations, and consistent client outcomes at scale. The move signals where advisory models are heading: augmented teams, tighter workflows, and measurable gains in engagement and AUM growth.
What's changing
Arta AI provides advisors with dedicated AI agents that sit across the bank's data stack. These agents surface insights, automate routine analysis, and support day-to-day decision-making, cutting cycle times from inquiry to recommendation.
The result: fewer manual handoffs, cleaner context in every client interaction, and a more defensible audit trail for how advice is formed.
How each bank is using it
Bank of Singapore is deploying Arta AI to assist external asset managers and family offices. The focus is automating investment research and personalising portfolio management, enabling deeper, more bespoke advice without increasing headcount.
Hong Leong Bank is equipping relationship managers with integrated portfolio data, CIO research, and risk models. The goal is to deliver recommendations that fit each client's risk profile while supporting the bank's broader push to embed AI across advisory-led wealth services.
What Arta AI brings to the table
The platform combines generative AI with real-time market data and proprietary research. Core features include portfolio analysis, Monte Carlo simulations, and multilingual communication.
It supports white-label deployment and firm-specific customisation, making it easier to slot into existing banking systems without disrupting frontline workflows.
Why this matters for management
- Productivity gains: Reduce research prep and proposal cycles so advisors can spend more time with clients.
- Consistency with personalisation: Standardised models and CIO views delivered in the context of each portfolio.
- Risk discipline: Clear parameters and scenario tools help align recommendations with client profiles and policy.
- Talent leverage: Lift the output of every RM and analyst; onboard new hires faster with embedded guidance.
- Client experience: Faster answers, proactive ideas, and communication in the client's preferred language.
Governance and implementation notes
- Data controls: Map sources (portfolio, CRM, research), set permissions, and audit access.
- Model oversight: Define review thresholds, escalation paths, and capture rationale for key recommendations.
- Integration: Connect to existing PMS/CRM tools and CIO research so advisors work in one flow.
- Change management: Train by role, provide playbooks, and set clear "dos and don'ts."
- Measurement: Track time-to-proposal, meeting prep time, adoption, win rates, and RM book growth.
Immediate next steps
- Select a pilot segment (e.g., affluent or select family offices) with clear success criteria.
- Prioritise 2-3 use cases: proposal creation, rebalancing prompts, and risk scenario reviews.
- Integrate core data feeds, then add CIO notes and product shelf details.
- Set guardrails for recommendations, disclosures, and client communications.
- Run side-by-side tests for four to six weeks; compare output quality and cycle times.
- Scale in waves once KPIs hold and governance is baked in.
If you're building internal capability or training teams on AI in finance, this curated list of tools is a useful starting point: AI tools for finance.
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