Exploring AI Adoption in BPO Reconciliation Services
How are BPO firms using AI in reconciliation? Leaders from Linedata, Tech Mahindra, ProConsult IQ and Operartis discuss what is driving adoption, which use cases to start with, whether to build or buy, and how to keep people in control.
Interest is running ahead of mandates. Most attendees joined to keep up with the technology, and very few had been asked to look at AI. The panel favoured AI as the maker and people as the checker, and buying over building.
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ON-DEMAND WEBINAR
Exploring AI Adoption in BPO Reconciliation Services: Challenges and Progress
A 60-minute panel for financial services leaders and their clients and partners on how AI is changing reconciliation (R2R) in business process outsourcing (BPO) firms.
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Speakers
Tracey Lall, Marc McCarthy, Marcela Crossman, Prashant Mishra and Manish Upadhyay.
What's driving AI adoption in BPO reconciliation?
Demand is coming from clients and from inside BPOs at the same time. Manish Upadhyay described reconciliation moving “from the manual led cost center to a value creation center”.
the drivers
Rising volumes.
As Manish Upadhyay put it, “Putting more bodies or humans on reconciliation process is definitely not a scalable solution”.
Richer, messier data.
ISO 20022 payments carry far more fields, and client data arrives in more formats every year.
Cost and accuracy pressure.
Marcela Crossman’s top drivers were efficiency, and accuracy with less operational and key-person risk.
Interest ahead of mandates.
71% of attendees came to keep up with the technology, 24% to improve efficiency, and only 5% had been asked to look at AI.
Which use cases should a BPO start with?
Transaction matching.
Start where effort and repeatability are highest: automated matching, including real-time and self-learning reconciliation.
Breaks and root cause.
Attendees ranked breaks handling and root-cause analysis as the top AI use case, at 37%.
Exception routing.
Send each exception to the right team automatically, with the context needed to resolve it.
Unstructured data.
Turn statements, emails and PDFs into structured data the matcher can use.
Data quality decides the result.
Whatever the starting point, data quality still decides the result, as Marcela Crossman stressed.
Should BPOs build or buy AI?
Mostly buy, and keep in-house tools modular enough to plug in what you buy. Marcela Crossman put the split at roughly 90% buy, because building matching models in-house rarely pays back for a service provider.
Roughly 90% buy
Building matching models in-house rarely pays back for a service provider, in Marcela Crossman’s view.
Builds get abandoned
Marc McCarthy has seen in-house builds dropped after six months and half a million dollars.
Partner first
Manish Upadhyay explained that Tech Mahindra partners first and learns a product’s limits before deciding whether to build.
Prove it on client data
Attendees ranked proofs of concept as the most popular way to evaluate AI vendors.
Maker-checker
AI proposes and people approve, until the results are proven for that use case.
A governing body
Put a centre of excellence over AI use, and expect your clients' model risk teams to review it.
Evidence clients can use
Clients stay accountable for sign-off, so give them documentation and validation evidence they can take to their risk committee.
Industry certification
The panel called for AI certification in reconciliation, so firms stop inventing their own approval processes.
Cloud, hybrid or on-premise?
Three of the four panellists preferred cloud, because it makes working with data at scale easier. Manish Upadhyay chose hybrid, because sensitive client data sometimes has to stay with the client.
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Cloud
The easiest option for working with data at scale.
Hybrid
Keeps sensitive client data in the client's environment while the service is shared.
On-premise
Runs inside the client’s own infrastructure when data cannot leave it.
Modular add-ons
Attendees’ preferred option and the lowest risk, because one that does not work out can be switched off.
Will AI replace reconciliation jobs?
A cautious opportunity
Manish Upadhyay’s phrase. Matching is repetitive work that lands first thing in the morning, when people are sharpest.
Roles will change
Removing matching frees people for higher-value work, but they will need to upskill.
BPOs hold an advantage
They see best practice across many clients and can build it into their AI services.
Jump to the part you need
00:00 Introductions
05:15 Poll: why attendees came
07:21 What's driving adoption
13:45 Data complexity and peer pressure
15:51 Building internal expertise
20:03 Choosing use cases
30:27 Use-case poll
31:30 Build vs buy
41:56 Risk, governance and compliance
46:13 Deployment models
49:23 The future of BPO and agentic AI
54:38 Q&A
AI in BPO Reconciliation FAQs
1. How can a BPO offer AI matching to its clients?
Through technology licensing, a revenue share or a co-branded service. Partner Solutions sets out each model.
2. Should a BPO build or buy AI for reconciliation?
Mostly buy. Panellists put it at around 90% buy and have seen in-house builds abandoned after six months and half a million dollars. Keep in-house tools modular so you can plug in what you buy.
3. Do clients have to replace their reconciliation systems?
No. Matchimus runs alongside existing platforms such as IntelliMatch, TLM or Duco and reads the same data the matchers use.
4. How is client data protected?
Data is anonymised before it reaches Operartis, or Matchimus runs inside the client's own environment.
5. How should a BPO evaluate AI vendors?
Run a proof of concept on representative client data and measure match rate and accuracy, not features.
6. Will AI replace reconciliation jobs?
The panel saw it as a cautious opportunity. Matching work goes and people move to higher-value work, so plan for upskilling.
7. Does AI help insurance reconciliations?
The panel thought so, especially for IFRS 17, where complex underwriting calculations need reconciling and explaining.
Your next step
01
Matchimus runs alongside IntelliMatch, TLM or Duco and reads the same data the matchers use.
02
Accountability, validation and monitoring for AI matching, from our model risk panel.
Bring AI matching to your clients
Tell us about your clients and the reconciliations you run for them, and we'll suggest a partnership model. Want to see results on a client's data first? Book a Proof of Value.