International Payments Ops Workflow Redesign: Reduce Million Dollar Losses from Unvalidated Payments

Role

Everything - Program-level influencing, strategic research

Duration

Initial program kick off – 4 weeks,
part time
; Lean research – 3 weeks

Organisation type

Large org - Big 4 Bank

Project year

2023

Program-level
context

  • There was a multi-million dollar investment in the International Payments portfolio to increase traceability, speed and data-richness.
  • I lead a cross-functional team to rank priority for over 30+ complex, interdependent International Payments use cases that spanned Business, Personal, Ops and Wholesale.
  • Our prioritisation determined how we’d sequence the use cases and whether there was enough investment to address priority customer pain points like fees and unsuccessful payments.

Project-level
context

Our International Payments ops teams used 6 systems that presented the following challenges:
  • Legacy tech = high development overhead for new functionality – affected a key exec scorecard metric.
  • Lack of payment validation checks = $$$ losses – high value payments would be sent to the wrong recipient with no guarantee of retrieval.
  • Unnecessary manual work = higher FTE head count - e.g. data was sometimes incorrectly ingested when passed between systems so required manual intervention.
  • Difficulty cross-skilling or utilising resources across ops teams - ops systems are all very different.

Design approach

Sell in Discovery. 3rd time's a charm.

I attempted to sell in Discovery 3 times.
For the 2nd attempt: I met the Senior Product Owner, Product Owner and Lead Business Analyst to walk them through why Discovery was important + how it worked. They communicated that their top 2 priorities were to meet compliance deadlines and ensure the backend architecture was future-proofed.

I revised my pitch with a peer, and lead another conversation with:
"I understand your priorities. If we want to future-proof the architecture, we need to know the future state design direction, because the it dictates how we'll build the architecture in the longer term. This is going to save the program millions in the long run."
They agreed to invest in a 3 week Discovery Sprint.

Determine scope

I partnered with a senior ops team member to understand the foundations of ops - systems, team structure, KPIs, pain points, jobs-to-be-done.
The Product Owner, Business Analyst and I  worked together to decide which systems to focus on for a 3 week Discovery sprint - we couldn’t get across all six systems in-depth within the timebox. 

We chose eOCP and ITF because they were:
A) the most used systems, and
B) less dependent on other systems, which increased the chances of shipping a release sooner.

AI design workflow

Problem solving how to revise my first sell-in pitch
Consult Claude AI after I understood why Product didn’t want to initially invest in the Discovery process. Compared to other AI’s, one of Claude’s strengths is providing advice for situations that require emotional nuance.

AI-assisted workflow

Problem solving how to revise my first sell-in pitch
Consult Claude AI after I understood why Product didn’t want to initially invest in the Discovery process. Compared to other AI’s, one of Claude’s strengths is providing advice for situations that require emotional nuance.

Review current state

I reviewed system documentation and paired with our Business Analysts to download as much info as possible. They’d had a strategic head start in context building.

Example insight:
A key scenario where ops need to initiate a payment:
The FX markets are closing for the day, and the customer needs to make a high value or crucial payment (e.g. to honour a contract)
via a system that doesn’t permit straight through processing.

Ontology mapping

Ops speak and operate in a whole other language and world.

Example:
“So using PACs terms, for Day 2 payments we can add the full range of instructing or instructed agents in ITF the way we could if keying a message manually for MT104 and 103s.”
- Senior Analyst, International Payments
By systematising the complexity, I could better articulate how to connect the different aspects of the customer experience together AND provide foundational knowledge for how we might phase the approach with the Product Owner.

AI-assisted workflow

Ontology mapping using training material
If there was significant, centralised ops team member training material (e.g. wikis, user manuals for key systems, system error reports), I could ask ChatGPT(Plus) to generate a first-draft ontology map.
The aim would be to define domain-specific jargon in simple terms, map how it all relates to each other, quality check it with an experienced team member then disseminate within the squad to speed up with consistent context building.

Async pain points exercise

I sent out an sync task above to inform the current state customer value map, and would answer any knowledge gaps during stakeholder interviews.

Stakeholder interviews

I interviewed the ops Heads Ofs, execs, retail and backend individual contributors. For senior leadership stakeholders I focussed on KPIs, $$$s and the wider market. For individual contributors, I focussed on jobs-to-be-done, learning ops concepts and questions from the pain points exercise that needed further clarification.

Example insight:
“We had a $(5-figure) loss in one day from one generic non-validation issue.”
Image created using Midjourney AI.

Consult analytics

I requested everything from average time to complete key tasks, customer segments, volume and $ value of payments on the different ops systems to:

A) Gain a baseline for the current state customer experience, and 
B) Inform the business case with numbers around $ loss risk for each platform.

Concept and recommendations

The stakeholder feedback was that we needed to quantify the impact of some of the pain points:

e.g. What’s the time / cost associated with the workflow disruption pain points if any?
Given the short timebox, we needed to explore this in detail.

AI-assisted workflow

First-draft research synthesis
So far, ChatGPT has produced mid-weight researcher level insights. They’re still quite generic so require user oversight and time to extract nuance. Kraftful is great for summarising pain points, but not yet capable of writing quality free-form insights.

Outcomes

Program-level

By taking the time to have a dialogue with stakeholders during the Strategy Sprint, we articulated a clear cost out dollar figure.
Senior stakeholders could then create a strong business case informed by company priority.


• I shifted our stakeholder group towards customer-focussed outcomes e.g. which systems contributed the most to unsuccessful payments / contributed to fee disputes – instead of just a blind re-platform of everything.

Project-level

Two key ops systems were strategically sliced into smaller phases, providing speedier value to market.

• Pain points and dependencies were highlighted, which provided stakeholders with a view of HOW to strategically re-platform instead of wasting their project budget on a single bloated release that never gets shipped.