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CS-Industry: Fintech

Compliance and Decisioning in North America - Claire Hartley

From Regulatory Complexity to Competitive Confidence: How Provenir Supports Responsible Decisioning Across North America

FFrom Regulatory Complexity to Competitive Confidence:
How Provenir Supports Responsible Decisioning Across North America

Across North America, financial services providers must make faster, more accurate decisions while meeting increasingly complex regulatory expectations. In the United States, federal obligations sit alongside a growing patchwork of state privacy, cybersecurity and artificial intelligence laws. In Canada, organizations must address PIPEDA, provincial privacy requirements and guidance from the Office of the Superintendent of Financial Institutions.

For organizations using data and AI in credit, fraud and identity decisions, compliance cannot be a final check. It must be built into the way technology is governed, operated and monitored.

Governed and Explainable Decisioning

Provenir brings data, AI models, analytics and automated decisioning together within a single, governed environment. Customers retain control over their decision strategies, rules and compliance guardrails, with the ability to test, approve, monitor and adjust them through controlled processes.

This matters most in US credit decisioning. The Equal Credit Opportunity Act requires lenders to provide specific and accurate reasons for adverse decisions, even when AI or complex models are used. Provenir supports transparent decision logic, configurable reason codes and accessible decision information, helping customers explain outcomes and maintain an effective audit trail.

The customer remains responsible for its lending decisions and legal obligations. Provenir provides the governed technology and control capabilities that help it meet them.

Strong Privacy, Security and Assurance

Provenir operates a mature privacy and data protection framework covering data-processing agreements, controller and processor responsibilities, international transfers, retention, data subject rights, subprocessor oversight and privacy risk assessments. These controls help customers understand how and where personal information is processed, and provide evidence of the safeguards applied throughout the processing chain.

Independent assurance carries equal weight. Provenir maintains ISO/IEC 27001 certification for its Information Security Management System and has completed a SOC 2 Type II assessment covering security, availability and confidentiality. Enterprise customers can also access appropriate assurance evidence, subject to scope and confidentiality requirements, including security and privacy information, penetration-testing summaries, business continuity information and AI governance documentation.

This evidence supports vendor due diligence and helps customers demonstrate effective third-party oversight. It is particularly relevant to the US Gramm-Leach-Bliley Act Safeguards Rule and to Canadian expectations under OSFI Guidelines B-10 and B-13 concerning third-party, technology, cybersecurity and operational resilience risk.

Responsible AI With Practical Controls

Responsible AI requires more than a policy. It needs defined accountability, risk assessment, testing, documentation, human oversight, performance monitoring and controlled change management. Provenir is developing its AI governance framework in alignment with ISO/IEC 42001 and emerging regulatory requirements, building on its established compliance, privacy, risk and information security controls.

Compliance as a Customer Advantage

Provenir does not claim to make customers automatically compliant. It gives financial services providers across North America a controlled and transparent decisioning environment, backed by mature governance and independent assurance.

This helps customers manage regulatory scrutiny, complete enterprise due diligence more efficiently and innovate without losing control. In a market where trust increasingly determines which technology providers can support critical financial processes, Provenir’s Compliance, Regulatory and Data Protection capabilities do more than support the business. They turn assurance into advantage, and that is a genuine competitive differentiator.

Claire Hartley, Chief Compliance Officer Group DPO, Provenir

Claire Hartley

Written By

Chief Compliance Officer & Group DPO, Provenir

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Claire Hartley - APAC Compliance Challenge

APAC’s New Compliance Challenge

APAC’s New Compliance Challenge:
Managing Innovation Across a Fragmented Regulatory Region

Asia-Pacific is entering a more demanding phase of compliance, regulatory and data protection oversight.

The challenge is no longer simply keeping pace with new legislation. Organisations must now manage an increasingly complex combination of national privacy laws, data localisation requirements, international transfer restrictions, artificial intelligence governance and sector-specific regulation.

Unlike the European Union, APAC does not operate under one harmonised regulatory framework. Each jurisdiction has its own legal requirements, regulatory priorities and enforcement approach. A control that is appropriate in Singapore may not be sufficient in Australia, China, India, Indonesia or New Zealand.

This fragmentation creates a significant challenge for organisations operating across multiple APAC markets.

AI and Automated Decision-Making

Artificial intelligence is becoming inseparable from data protection and regulatory compliance.

In July 2026, Singapore’s Personal Data Protection Commission published guidance addressing how personal data should be collected and used throughout the generative AI lifecycle. This includes web scraping, reusing existing customer data, allocating responsibilities between AI providers and users, managing retention and responding to individual rights requests.

Australia is also introducing new transparency requirements for automated decision-making. From 10 December 2026, regulated organisations will need to disclose certain uses of personal information in computer-generated decisions that could significantly affect an individual’s rights or interests.

For financial services organisations, these developments create particular challenges. AI and automated models may influence credit, fraud, identity, affordability and customer-management decisions. Organisations must therefore be able to explain not only what a model does, but also how data is selected, how risk is assessed and where human oversight is applied.

Biometrics and Identity Data

Biometric information is also receiving greater regulatory attention.

New Zealand’s Biometric Processing Privacy Code introduces specific rules governing the collection and use of facial, fingerprint, voice, behavioural and other biometric information. Organisations already using biometric processing must transition to the new requirements by 3 August 2026.

This reflects a wider regional trend. Identity verification and fraud-prevention technology can deliver substantial benefits, but regulators increasingly expect organisations to demonstrate necessity, proportionality, transparency, security and appropriate retention.

Data Transfers and Local Requirements

Cross-border data transfers remain another major area of complexity.

APAC organisations frequently rely on global cloud infrastructure, regional service providers and international data sources. However, the legal mechanisms for transferring personal data differ significantly between jurisdictions.

Some countries require contractual safeguards. Others may require regulatory assessments, certifications, local storage or additional controls for particular categories of information. Organisations therefore cannot rely on one global transfer mechanism without first assessing the law, data and processing activity in each relevant market.

The practical challenge is knowing where information is located, how it moves, which providers can access it and which organisation is accountable at every stage.

How Provenir Meets These Demands

Provenir addresses these challenges through a global Compliance, Regulatory and Data Protection, or CRDP, framework that combines central governance with jurisdiction-specific regulatory analysis.

CRDP provides independent oversight and challenge across Provenir’s platform and works alongside Information Security, Product, Engineering, Technology and Legal. This enables regulatory requirements to be considered throughout product development, contracting, implementation and ongoing customer support rather than only at the final compliance review stage.

Provenir’s approach includes:

  • – privacy and data protection assessments for new technologies, products and processing activities;
  • – governance of international transfers, subprocessors and regional data flows;
  • – due diligence and risk assessment for vendors and data providers;
  • – defined incident identification, escalation, investigation and notification processes;
  • – data-minimisation, retention and access-control requirements;
  • – regulatory monitoring across the countries in which Provenir and its customers operate;
  • – documented accountability for AI, model governance and automated decision-making.

Provenir also uses a structured lines-of-defence model. Operational teams own and manage their controls, CRDP provides policy, advice, monitoring and challenge, and independent certification and assurance activity provides further scrutiny.

Privacy governance is embedded within how Provenir designs, deploys and operates its platform. This includes clear controller and processor role allocation, data-processing agreements, privacy impact assessments, international transfer safeguards, subprocessor oversight and breach-management procedures.

AI governance is similarly integrated across CRDP, Product, Engineering and Information Security. Provenir’s framework addresses purpose, accountability, data governance, fairness, transparency, human oversight, security and continuing monitoring, with reference to ISO/IEC 42001 and emerging regulatory requirements.

The platform itself supports this governance approach by bringing data, models and decisioning into a controlled environment. This gives customers greater visibility over decision strategies, testing, deployment and performance, while allowing them to apply their own regulatory policies and risk controls.

From Compliance Obligation to Market Confidence

The most successful organisations in APAC will not be those that attempt to apply one policy everywhere.

They will be those that establish consistent global governance while retaining the flexibility to respond to local laws, regulatory expectations and customer requirements.

For Provenir, strong CRDP governance is not separate from innovation or commercial growth. It provides the structure required to deploy data and AI responsibly, support customers across different regulatory environments and enter new markets with greater confidence.

In a region defined by rapid technological development and regulatory diversity, this ability to combine innovation with demonstrable control is becoming a significant competitive advantage.

Claire Hartley, Chief Compliance Officer Group DPO, Provenir

Claire Hartley

Written By

Chief Compliance Officer Group DPO, Provenir

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Customer Story: Varo

varo

Fintech startup Varo Bank is one of several fintechs and “challenger banks” in the United States aiming to take on the big traditional financial institutions by catering to the customers those institutions neglect: people with lower incomes and little wealth. The bank is completely digital with no brick-and-mortar branches.
  • Industry
  • Region
  • Country

    United States

  • Line of Business
  • Solution
  • Module
  • Infrastructure
  • ROI
  • Competition

Customer Timeline
Land MRR: $13,513
Land PS: $196K est
Expand MRR: TBD
Expand PS: $52,740
  • Opportunity Created
    June 20, 2020
  • Opportunity Won

    January 18, 2022

    The decision timeline was significantly delayed due to the ongoing COVID-19 pandemic.

  • Go-Live

    April 17, 2024

    Reorganizations and multiple project holds from Varo affected the timeline. Actual implementation took 5 months.

  • Customer Expansion
    • The original plan (2022) was to expand usage to all consumer products.
    • The first go-live (April 2024) was to support an employee line of credit.
    • Current plans are to expand to customers in May 2024.
    • Provenir is finalizing a PS Consulting SOW for $52,740, expected to close in May 2024.
Initial Opportunity Details

  • Customer Challenge

    Varo’s initial tech stack included an in-house-built mobile application and Temenos as the core banking system. By 2020, Varo was on the cusp of receiving its banking license and went to market to find an origination solution in anticipation of rolling out a variety of consumer loan products and credit cards.

    In looking at other vendors + the possibility of leveraging older in-house-build applications to create an in-house solution, the ability to configure rapidly, test efficiently including use of A/B testing, and expand easily in the future to new use cases were identified as key drivers of the decision.

  • Provenir Approach

    • The initial POC was more complicated from a functionality perspective than the finalized scope of the LOC decisioning process put into production in April 2024.
    • Nonetheless, Provenir’s initial reasons for selection – ability to rapidly configure business objects and deploy models – were both used to support the eventual as-deployed project, with numerous rule sets and Python models part of the configuration.
  • Provenir Impact

    Varo had no previous origination system and as a de novo solution, no before-and-after comparisons are possible.

  • Competitors

    GDS Link, In-House Build (finalists) + Zoot, Alchemy
  • Why We Won

    Competitive Pricing, Product Functionality – especially object configuration and model execution, Relationship with executives built over several years.

    GDS took 3+ weeks to configure/deliver custom demo requirements; Provenir did a better custom demo only a week after receiving requirements.

  • Pain Points

    Temenos (Varo’s existing core system of record) didn’t have origination capability. Thus, they were in the market for an origination solution.
Customer Growth

Growth Opportunities

  • The initial license was only ringfenced based on DE utilization and “consumer lending.” At this time, additional expansion will only likely affect DE utilization as Varo is not currently planning expansion beyond consumer products.
  • Case Management has been something in which Varo has shown interest in the past; this should be revisited at a later date and is considered “closed nurture” today.
  • Lexis Nexis is interested in partnering with Provenir to sell data into Varo.

Expansion

  • So far, with so many starts and stops of the implementation project (all initiated by the Varo team), getting the initial project live has been the focus.
  • At the time we were named the selected vendor, the plan was to leverage Provenir across all decisioning as Varo introduced new products. Even though Varo’s timeline to roll out new products has greatly increased, Provenir is still positioned to support these initiatives when they happen.
Example Decisioning Flows
  • New Application

    Decisioning

  • Bureau Integration

    Decisioning

    circulo
  • Rules and Models

    Decisioning

    • Post-Bureau Knockout Rules
    • Python Models
    • Post-Model Knockout Rules
  • Credit Line Assignment

    Decisioning

    • Credit Line Grid
    • Line-Capping Rules
  • Compliance

    Decisioning

    Compliance Rules
OTHER CUSTOMER STORIES

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Customer Story: Jeitto

Jeitto, a Brazilian mobile-first neobank founded in 2014, offers credit, payment, and other financial services to underserved consumers. Through its user-friendly app, Jeitto empowers consumers to better manage their daily finances, including utilities, transportation, and mobile top-ups. The company fosters financial confidence in consumers with limited financial experience by rewarding positive behavior and progressively introducing a broader array of financial services. As of December 2023, Jeitto recorded 4+ million subscribers and US$ 25+ million in originations per month.
  • Industry
  • Region
  • Country

    Brazil

  • Line of Business
  • Solution
  • Module
  • Infrastructure
  • ROI

Customer Timeline
Land MRR: 35,230
Expand MRR: 22,000
  • Opportunity Created
    09/09/2022
  • Opportunity Won
    01/30/2023
  • Go-Live
    • 05/17/2023
    • 04/25/2024
  • Customer Expansion
    • 04/16/2024 – Capacity increase

    • TBD – New LOBs requested by the client

Initial Opportunity Details

  • Customer Challenge

    Jeitto faced challenges in credit evaluation speed, risk decision-making, and process complexity. Initially, Jeitto’s credit evaluation process took nearly 1.5 minutes, and they aimed to improve their risk control and fraud prevention to achieve more agile and efficient workflows.

    The involvement of the technology team was required for changing decision flows, which was a time-consuming process involving planning, development, and testing.

  • Provenir Approach

    Jeitto reduced the credit evaluation time to just 30 seconds, doubled their customer base, increased approval rates by 10%, reduced delinquency by 30%, and saw an 8% rise in the average ticket size. The enhanced data processing speed also increased the capacity for reanalysis of initially denied credit applicants, with 20-25% of these customers obtaining a loan later. Provenir’s platform allowed Jeitto to make more intelligent, layered decisions, saving on data consultation and analysis costs, and simplified processes, freeing the technology team to focus on business areas.
  • Provenir Impact

    • Credit risk decision takes 1/3 of the time it took before implementing Provenir
    • 10% approval rate increase
    • 8% bookings increase
    • The use of all available data in real time has resulted in a 20%-25% approval rate increase from those declined in the past.
  • Competitors

    Jeitto also looked at FICO, Neurotech, Crivo (TU), and local peers Stoneage and Ibratan
  • Why We Won

    • We showed more value-add than competitors and client’s internal dev
    • Jeitto realized that the Provenir solution deployment fit their growth projections for 2023
    • Very collaborative relationship between Jeitto and Provenir
    • Pricing
  • Pain Points

    • Slowness of the process, reducing capacity to increase the operation
    • Manual operation, increasing error risk
    • New business demands technology breakthrough
Customer Growth

Growth Opportunities

• Case Management
• New LOBs requested by the client

Expansion

As result of the Provenir solution, in which the number of clients doubled and the response time fell from 90 seconds to 30 seconds, Jeitto asked Provenir to increase solution infrastructure.
Example Decisioning Flows
  • Delinquency Case

    Collections

    • Gets debt data from internal DB
  • Filters & Calculations

    Collections

    • Checks eligibility
    • Calculates days of arrears
    • Checks score
    • Calculates number of agreements
  • Define Actions

    Collections

    • Checks what are the next actions
    • In case of new offer, calculate number and value of instalments
  • Execute Actions

    Collections

    • In case of new offer, assigns fees, values
  • Make Decision

    Collections

    • Executes action plan
  • New Application

    Origintations

    Applies initial filters:

    • New client or client
    • Calculate age
  • Filters

    Origintations

    Gets bureaus data:

    • IRS (fiscal status)
    • Bureaus: PH3A, SPC, Novavida, Quod, Socinal, Serasa, Vivo, BVS
  • Fraud Check

    Origintations

    • Checks phone calls consistency
    • Check address
  • Scoring

    Origintations

    • Calculates score (python model)
  • Decision

    Origintations

    • Executes decision rules
  • Jeitto Client

    Pre-Approved Loans

  • Inquiry Internal Behaviour & Info

    Pre-Approved Loans

    • Debts
    • Internal score
    • Income and other data
  • Inquiry External Data

    Pre-Approved Loans

    • IRS (fiscal status)
    • Serasa, BVS, SPC, Quod, Socinal, Vivo
  • Define Amount & Conditions

    Pre-Approved Loans

    Runs model (python) to:

    • Define product
    • Define amount to be lent
    • Define payment conditions
OTHER CUSTOMER STORIES

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