The last time a fintech company hit the news, most people read about it and moved on with their day.

The people inside the company didn’t. Security teams stayed late. Executives had calls scheduled with regulators before the story broke. Support lines filled up with customers asking whether their money was safe, and the answer wasn’t simple enough to fit in a phone script.

That’s what a breach actually looks like from the inside. Not a headline. A week of people trying to figure out what happened and who to tell, while the systems everyone depended on still need to keep running.

Regulators have gotten sharper about this. Penalties scale to how much damage a breach causes rather than sitting at a fixed number. Customers move their accounts somewhere else. And the architecture underneath all of it was often assembled over years, piece by piece, by people who didn’t expect the threats the bank faces today.

That’s the world the fintech cybersecurity services industry grew up in.

What Makes Financial Systems Different

A retailer loses transaction data and deals with the fallout. A bank faces something worse. The money is part of it. Attackers know the payoff is bigger, so they bring more resources and more patience. But the harder problem sits underneath. Financial data is regulated in ways retail data isn’t, so every incident triggers reporting deadlines across multiple jurisdictions at once. Legal, compliance, and PR teams all get pulled in on day one.

Then there’s the customer side. People don’t switch banks over a delayed refund. They switch when they don’t trust the institution with their money anymore, and that decision usually happens once.

The infrastructure stopped being simple a while ago. A bank might keep its core ledger on hardware it owns, run customer apps on one cloud, analytics on another, and let vendors handle identity checks. Nobody drew this on a whiteboard. It grew.

Every connection has credentials and permissions, and every one is a place something can go wrong. Few security teams built this. They walked into it.

FinTech cybersecurity services teams handle this by assuming something will slip through. The work is surviving that moment. Systems stay up. Customers get told quickly. Regulators get their reports on time.

Recovery is where most programs fail. Blocking is easy to sell. Cleaning up takes planning nobody wants to do.

The Rules Keep Multiplying

DORA went live in January 2025. It changed how financial institutions get supervised. Instead of annual reviews, the expectation is that evidence can be produced whenever a supervisor asks for it. That includes the register of ICT providers, which has to be accurate on the day it’s pulled.

The penalty structure scales with turnover. A smaller institution owes less when something goes wrong. But the reporting work doesn’t scale the same way. A compliance team of three people at a credit union produces the same documentation as a team of thirty at a global bank. Same forms, same deadlines, different resources.

Then there’s the pile. PCI DSS, GDPR, ISO 27001. Each framework overlaps with the others, which means one control sometimes satisfies multiple requirements. The waste comes in the formatting. Every regulator wants the same evidence presented differently, so teams end up producing the same proof three times in three different layouts.

Experienced compliance teams have learned which controls pull double or triple duty. They’ve also learned which requirements only look satisfied until a real incident tests them.

Where the Weak Points Are, and What Fixes Them

Breaches trace back to a short list of failures.

• Access controls drift. IAM roles pick up wildcard permissions. Security groups open during maintenance and never close. One account with too much access becomes the path to everything.
• Data sits unencrypted. Backups included.
• APIs are the newer problem. Open banking multiplied exposed endpoints, and banking sees some of the highest rates of API incidents of any industry. Broken authorization, excessive data exposure, thin rate limiting. Scanners miss the business logic flaws specific to finance.
• Shadow APIs never make it into the inventory. Nobody knows they exist until someone exploits them.
• Supply chain adds another door. Vendors, CI/CD pipelines, SaaS admin credentials. These now rival phishing as an entry point.

Gaps can be closed by Avenga through continuous monitoring, proactive vulnerability search before any attacker reaches it, and threat simulation on live infrastructure. Pentesters include all modes of testing, starting from blind mode without internal data up to the mode with total access, aligned with ISO 27001, PCI DSS, NIST, and OWASP.

Finance sector companies operate in pentesting programs permanently rather than conducting one-time assessments. SOC manages to process alarms that appear because of such monitoring. Long-term customers never had SLA failures.

Regulatory and Data Compliance

GDPR, PSD3, PCI DSS, and DORA all land on the same team. Avenga uses AI tooling for real-time monitoring and automated reporting, which trims the paperwork that usually eats a compliance officer’s week.

DORA gets its own attention. Avenga handles the gap analysis, maps out what needs fixing, and stays on through implementation.

That last part matters. DORA isn’t a project you finish and walk away from. Requirements change, supervision tightens, and the reporting load stays steady even after a clean audit.

Legacy Modernization and Integration

Core banking systems usually existed before the risks they are now subject to appeared. It is impossible to rewrite them completely, so Avenga modernizes them on-site while preserving all of the compliance requirements.

It means migrating workloads to the cloud infrastructure when possible, replacing those elements that cannot be secured anymore, and making sure each connection added does not leave an open backdoor.

DevSecOps and IT Operations

Security is built in from the start rather than being added to later. DevSecOps by Avenga encompasses automation, secure development techniques, and cloud operations, which provide security while not impeding speed.

What Good Protection Looks Like in Practice

Frameworks and services play their role, but what really counts is the result of implementing those. Some common denominators for companies succeeding in this area include:

• Zero Trust approach as an operational principle. Access control decisions depend on continuous real-time assessment of the user, device, workload, and context. Pre-arranged standing trust using VLANs and networks no longer meets requirements.
• Microsegmentation. Communication between workloads happens exclusively between authorized ones. The compromised credential does not allow access to all resources.
• Shift-left security approach. Static and dynamic assessments, scanning in infrastructure-as-code and dependencies, are performed within the CI/CD pipeline. Issues are detected where developers work, not in a separate ticketing system weeks later.
• Threat intelligence in a continuous way. Industry-specific feeds, sharing through FS-ISAC, continuous threat hunting instead of reacting to incidents.
• Validated business recovery approach. Immutable and air-gapped backups, defined RPO/RTO targets, and tabletop exercises considering aspects of compliance and legal.

Why Fraud Teams Stopped Trusting Rules Alone

A rules engine works just fine until it does not work. Someone establishes a threshold – for example, transactions exceeding the set value made by a new device will be considered suspicious – and it does a good job catching fraud for a month. Then fraudsters learn about the threshold and split their payments under the threshold.

Rules have their place to use; they are responsible for dealing with the established patterns. Yet the weakness of the rules-based approach lies in its inability to identify a payment that matches the rules yet appears strange in relation to the particular customer’s pattern.

Here comes machine learning. It learns how to detect abnormal payments in the context of the individual customer’s behavior. Not the general average customer, but precisely this one’s typical transactions. If there is an unusual payment coming from a device the customer has never used during their usual sleeping hours, from the city the customer has never visited before – this is an abnormal payment, and the model detects it, while a rule engine fails.

The real challenge is what happens with the detected abnormal transaction next. It requires a manual investigation, with legitimate transactions being stopped and fraudulent ones being released. The feedback from both results goes back to the system and determines its further performance.

Bottom Line: Building Trust Through Resilience

Avenga works with banks, payment processors, and insurers. Their team covers compliance, cloud security, and application testing, which matters because most incidents touch all three at once.

Preparation shows up in the small hours. The difference between a contained incident and a full crisis usually comes down to whether someone on the other end of the phone knows what to do before the sun comes up.

The institutions that hold up treat security as ongoing engineering work. Regulations shift. Threats change shape. The ones still standing after a decade assumed both would keep happening.