AI Powered Payments Platform
Transformed a complex payment blotter into an AI-assisted command center, cutting task time to <3 minutes, reduced ticket allocation per team <2000 and improving multi-team operational clarity.
AI Powered Payments Platform
Transformed a complex payment blotter into an AI-assisted command center, cutting task time to <3 minutes, reduced ticket allocation per team <2000 and improving multi-team operational clarity.
AI Powered Payments Platform
Transformed a complex payment blotter into an AI-assisted command center, cutting task time to <3 minutes, reduced ticket allocation per team <2000 and improving multi-team operational clarity.
Status & Year
Launched & Awarded (2025)
Status
Launched & Awarded (2025)
Status & Year
Launched & Awarded (2025)
Company
JP Morgan
Company
JP Morgan
Disclaimer
This project is based on real-world experience in a regulated enterprise environment. Sensitive data, system identifiers, and workflows have been modified to maintain confidentiality.
Problem Statement & Challenges
The Payment Blotter was a powerful but fragmented enterprise platform managing high-value institutional transactions.
Over time, layered permissions, complex listing logic, and role-based visibility rules created inconsistencies in how different users interpreted the same payment data.
Users struggled to interpret payment states consistently, manage tickets efficiently, and understand why certain data appeared or remained hidden. The density of information increased cognitive load, slowed decision-making, and introduced operational friction in a time-sensitive environment.
My Contribution
As the lead designer on this initiative, I was responsible for auditing the existing workflow architecture, identifying logic inconsistencies across roles, and reshaping the blotter into a clearer, more predictable, and future-ready operational workspace while laying the foundation for AI-enabled enhancements.
Design
1 Member
Design
1 Member
Product
4 Member(s)
Product
4 Member(s)
Engg+Data+Ops
4 Member(s)
Engg+Data+Ops
4 Member(s)
Timeline
3 Sprint(s)
Timeline
3 Sprint(s)
Persona, Needs & Wants
To understand operational friction at scale, we conducted 12 stakeholder interviews across Operations Associates, Client Service teams, and Back-Office stakeholders managing high-value institutional transactions.
These roles operate in environments that are high-risk, time-sensitive, and compliance-driven, where even minor ambiguity can delay resolution or escalate risk.
Despite varying responsibilities, a consistent pattern emerged: the system provided data, but not clarity.
Our North Star
To transform the blotter from a dense monitoring interface into a structured operational workspace.
The goal was to reduce cognitive load, improve decision confidence, and preserve enterprise-level depth while making the system feel coherent, not overwhelming.
Research
12 User Interviewed
Research
12 User Interviewed
Market
US, UK (P1 Market)
Market
US, UK (P1 Market)
Activities
4 Service locations
Activities
4 Service locations
Timeline
4 Days (Mapping & Analysis)
Timeline
4 Days (Mapping & Analysis)
We began by mapping existing user journeys, auditing listing logic, and identifying inconsistency across roles and access layers. Whiteboard sessions uncovered how visibility rules and legacy decisions created fragmentation.
The redesign focused on: Simplifying payment state logic, Standardizing list behavior across roles, Structuring ticket handling flows, Improving contextual sharing mechanisms & Reducing visual density while preserving depth.
The system was designed not just to look simpler, but to behave predictably.
Ideate, Test & Measure
The new flow organizes payment data through clearer hierarchy and structured states. Role-aware access is surfaced transparently rather than buried in system logic. Ticket handling is embedded directly within contextual views, reducing back-and-forth navigation.
The new flow organizes payment data through clearer hierarchy and structured states. Role-aware access is surfaced transparently rather than buried in system logic. Ticket handling is embedded directly within contextual views, reducing back-and-forth navigation.
This creates a workflow where users:
Identify payment state faster,
Understand why they have access (or not),
Act directly from the listing view
Share relevant context without exporting.
This transformation repositions the Payment Blotter from a complex data table into a structured operational command center.
This transformation repositions the Payment Blotter from a complex data table into a structured operational command center.
By aligning visibility logic, simplifying interaction flows, and preparing the foundation for AI-driven enhancements, the platform becomes more predictable, scalable, and future-ready.
The key insight: in enterprise financial systems, clarity and consistency drive operational performance more than feature depth.
Impact
< 3 Mins (Ticket Actions)
Impact
< 3 Mins (Ticket Actions)
Impact
< 4 Mins (Ticket Processing)
Impact
< 4 Mins (Ticket Processing)
Impact
~ 75-80% (Task Satisfaction)
Impact
~ 75-80% (Task Satisfaction)
Impact
< 2000 (Tickets Processed)
Impact
< 2000 (Tickets Processed)
Continue Reading...
AI Powered Payments Platform
Transformed a complex payment blotter into an AI-assisted command center, cutting task time to <3 minutes, reduced ticket allocation per team <2000 and improving multi-team operational clarity.
AI Powered Payments Platform
Transformed a complex payment blotter into an AI-assisted command center, cutting task time to <3 minutes, reduced ticket allocation per team <2000 and improving multi-team operational clarity.
AI Powered Payments Platform
Transformed a complex payment blotter into an AI-assisted command center, cutting task time to <3 minutes, reduced ticket allocation per team <2000 and improving multi-team operational clarity.
Status & Year
Launched & Awarded (2025)
Status
Launched & Awarded (2025)
Status & Year
Launched & Awarded (2025)
Company
JP Morgan
Company
JP Morgan
Disclaimer
This project is based on real-world experience in a regulated enterprise environment. Sensitive data, system identifiers, and workflows have been modified to maintain confidentiality.
Problem Statement & Challenges
The Payment Blotter was a powerful but fragmented enterprise platform managing high-value institutional transactions.
Over time, layered permissions, complex listing logic, and role-based visibility rules created inconsistencies in how different users interpreted the same payment data.
Users struggled to interpret payment states consistently, manage tickets efficiently, and understand why certain data appeared or remained hidden. The density of information increased cognitive load, slowed decision-making, and introduced operational friction in a time-sensitive environment.
My Contribution
As the lead designer on this initiative, I was responsible for auditing the existing workflow architecture, identifying logic inconsistencies across roles, and reshaping the blotter into a clearer, more predictable, and future-ready operational workspace while laying the foundation for AI-enabled enhancements.
Design
1 Member
Design
1 Member
Product
4 Member(s)
Product
4 Member(s)
Engg+Data+Ops
4 Member(s)
Engg+Data+Ops
4 Member(s)
Timeline
3 Sprint(s)
Timeline
3 Sprint(s)
Persona, Needs & Wants
To understand operational friction at scale, we conducted 12 stakeholder interviews across Operations Associates, Client Service teams, and Back-Office stakeholders managing high-value institutional transactions.
These roles operate in environments that are high-risk, time-sensitive, and compliance-driven, where even minor ambiguity can delay resolution or escalate risk.
Despite varying responsibilities, a consistent pattern emerged: the system provided data, but not clarity.
Our North Star
To transform the blotter from a dense monitoring interface into a structured operational workspace.
The goal was to reduce cognitive load, improve decision confidence, and preserve enterprise-level depth while making the system feel coherent, not overwhelming.
Research
12 User Interviewed
Research
12 User Interviewed
Market
US, UK (P1 Market)
Market
US, UK (P1 Market)
Activities
4 Service locations
Activities
4 Service locations
Timeline
4 Days (Mapping & Analysis)
Timeline
4 Days (Mapping & Analysis)
We began by mapping existing user journeys, auditing listing logic, and identifying inconsistency across roles and access layers. Whiteboard sessions uncovered how visibility rules and legacy decisions created fragmentation.
The redesign focused on: Simplifying payment state logic, Standardizing list behavior across roles, Structuring ticket handling flows, Improving contextual sharing mechanisms & Reducing visual density while preserving depth.
The system was designed not just to look simpler, but to behave predictably.
Ideate, Test & Measure
The new flow organizes payment data through clearer hierarchy and structured states. Role-aware access is surfaced transparently rather than buried in system logic. Ticket handling is embedded directly within contextual views, reducing back-and-forth navigation.
The new flow organizes payment data through clearer hierarchy and structured states. Role-aware access is surfaced transparently rather than buried in system logic. Ticket handling is embedded directly within contextual views, reducing back-and-forth navigation.
This creates a workflow where users:
Identify payment state faster,
Understand why they have access (or not),
Act directly from the listing view
Share relevant context without exporting.
This transformation repositions the Payment Blotter from a complex data table into a structured operational command center.
This transformation repositions the Payment Blotter from a complex data table into a structured operational command center.
By aligning visibility logic, simplifying interaction flows, and preparing the foundation for AI-driven enhancements, the platform becomes more predictable, scalable, and future-ready.
The key insight: in enterprise financial systems, clarity and consistency drive operational performance more than feature depth.
Impact
< 3 Mins (Ticket Actions)
Impact
< 3 Mins (Ticket Actions)
Impact
< 4 Mins (Ticket Processing)
Impact
< 4 Mins (Ticket Processing)
Impact
~ 75-80% (Task Satisfaction)
Impact
~ 75-80% (Task Satisfaction)
Impact
< 2000 (Tickets Processed)
Impact