SunTec Unveils AI-Augmented Deal Management for Global Banks

SunTec Launches Enhanced Deal Management Product with AI-Augmented Automation for Global Banks

The announcement is brief, but the direction is clear: AI is moving into deal management for banking. If you run P&L, sales, or product, this points to faster cycles, tighter controls, and more consistent margins.

Why this matters for management

• Shorter deal cycles through automated pricing, approvals, and document generation.
• Stronger pricing governance and auditability across regions and segments.
• Higher margin capture with standardized terms and less manual error.
• Better client experience with faster, consistent responses.

What to ask the vendor before you commit

• Data integration: Does it connect to CRM, pricing engines, and core systems without custom rebuilds?
• Workflow: Can we configure approval paths, thresholds, and exception rules without code?
• Controls: How are audit trails, version history, and segregation of duties handled?
• AI transparency: What data trains the models, and how do we monitor drift and bias?
• Security and compliance: Region-specific data residency, privacy, and model risk requirements.
• Deployment: Cloud, on-prem, or hybrid options; performance SLAs; rollback plan.
• Cost and TCO: Licensing, usage tiers, integration, support, and ongoing tuning.

90-day implementation plan (practical)

• Days 0-30: Define 3-5 priority deal types, map current workflows, capture pricing rules, and baseline KPIs.
• Days 31-60: Integrate with CRM and pricing data, configure workflows, enable SSO, and set up audit logs.
• Days 61-90: Pilot with one region or segment, run dual controls, compare KPI shifts, and prepare go/no-go.

KPIs to track from day one

• Cycle time: Lead to approved proposal, and proposal to signed deal.
• Throughput: Proposals per RM per week.
• Win rate and average discount vs. policy.
• Margin leakage due to manual overrides.
• Compliance exceptions and rework rate.
• Manual touches per deal and time to first value.
• Client satisfaction post-proposal (short CSAT/NPS pulse).

Governance, risk, and controls

• Model risk management: Document models, training data, and monitoring cadence.
• Explainability: Clear rationale for suggested prices, terms, and approvals.
• Human oversight: Required checkpoints for high-value or high-risk deals.
• Data privacy: Mask sensitive data; enforce least-privilege access.
• Regulatory alignment: Map to regional guidance on AI and outsourcing.
• Fallbacks: Manual workflows and service degradation plans if AI is paused.
• Change management: Training, communications, and policy updates.

Tech integration checklist

• CRM: Bi-directional sync for contacts, opportunities, and activities.
• Pricing and product catalogs: Real-time access to fees, limits, and eligibility.
• Data pipelines: Clean, timestamped, and monitored for quality.
• Identity: SSO, MFA, role-based permissions, and approval hierarchies.
• Environment: Sandbox with anonymized data for testing and user training.
• Observability: Logs, metrics, and alerts for latency, errors, and anomalies.
• Version control: Configuration and policy changes tracked and reviewable.

Make the value case to the board

Frame outcomes in business terms, not features. Commit to a 6-9 month payback based on cycle time reduction, margin improvement, and lower rework.

• Benefits: 20-40% faster approvals, 1-3% margin improvement, fewer compliance exceptions.
• Costs: Licenses, integration, enablement, and internal support capacity.
• Risks: Model drift, user adoption, data quality. Mitigate with pilot gates and clear controls.

Keep compliance close

Align with current guidance on AI in financial services and document decisions. A good primer: FSB: AI and ML in financial services.

Upskill your team

Your frontline needs practical training on AI-assisted workflows, prompt quality, and oversight. If you want a curated view of tools and skills for finance leaders, explore: AI tools for finance and AI automation certification.

Bottom line

The announcement is short, but the signal is strong: deal management is moving to AI-augmented workflows. Treat this as an opportunity to standardize, speed up, and de-risk your commercial process-one controlled pilot at a time.