Insurance claims and accidents are not just an "unpleasant event" - for the CFO and risk manager, they are a recurring cost in premiums, service, vehicle downtime and often the company's reputation. AI driver scoring is an approach that combines telematics, driving style data and machine learning to measure, manage and progressively reduce risk. In this article, we explain how driver scoring works, what types of solutions are on the market, what to look out for (GDPR, AI regulation) and how to build your business case.

What is AI driver scoring

AI driver scoring is a method that assigns a risk score to each driver (or ride) based on real-world behaviour behind the wheel. The aim is not to "punish", but to:

To identify patterns of behaviour that increase the likelihood of an accident,
to set training, coaching or internal rules,
link the results to damage prevention and to the reduction of overall fleet costs.

What driver scoring typically assesses:

Hard braking and acceleration (aggressive driving),
speeding and limit crossing (contextual - urban/non-urban),
cornering and "G-force" (risky manoeuvres),
phone use while driving (if measured),
driving at night, at rush hour, on risky sections,
idling time, start/stop habits (impact on cost and wear and tear).

Why "AI": Modern models work better with context (road type, traffic density, weather - if it adds up), can detect more complex patterns, and improve over time on data from your fleet.

Why it's a topic for the CFO and risk manager

1) Insurance premiums and claims are rising - and the fleet feels it first

With corporate fleets, claims are not paid only once. It carries over to:

Insurance premiums (PZP/accident),
deductibles and uninsured parts,
service costs and spare parts,
downtime (replacement vehicle, logistics, loss of productivity),
internal costs of claims handling.

2) Prevention has the best "investment vs savings" ratio

Driver scoring is scalable prevention: even small improvements in driver behaviour can mean significant absolute savings for larger fleets.

3) Data = buying and insurance arguments

A well set-up program (telematics + scoring + coaching) provides numbers that can help:

In internal approvals (CFO/board),
when negotiating insurance terms,
checking car policy compliance.

How driver scoring works in practice

Step 1: Data collection (telematics)

Reliable data collection of driving data is essential. The most common forms:

Smartphone telematics (app): fast deployment, lower entry costs.
OBD dongle: relatively easy installation, stable data.
OEM/embedded telematics (from vehicle): high accuracy, less "extra hardware".
GPS unit + additional sensors: for specific needs and older fleet.
Video telematics (dashcam + AI): also captures risks not seen by conventional telematics (distance, distraction).

Step 2: Standardisation and context

Without context, data can be deceiving. A quality solution will take this into account:

Route type (city/off/highway),
time (night/peak),
fleet diameter, and type of operation (sales vs service vs logistics),
seasonality.

Step 3: Scoring and segmentation

The output tends to be:

Driver score (0-100 or A-E categories),
Risk situations (what specifically lowers the score),
comparison with the team/branch/region,
trend over time (improvement/deterioration).

Step 4: Action (coaching, policy, motivation)

The score is only meaningful when it translates into behavior:

Micro-coaching (tips after the ride),
training for "top risk" drivers,
car policy adjustment (phone calls, speed, working hours),
incentive schemes (gamification, safety bonuses).

Types of solutions on the market: what to compare before choosing

Below is a practical comparison (from a CFO/risk perspective) - without "vendor lock-in".

Solution type	How it works	Pluses	Cons	When it makes sense
Smartphone telematics	App measures driving and behavior	Fast deployment, low CAPEX	Need for discipline (BT/GPS on), occasional "false positives"	Fleet with drivers using company mobiles, fast pilot
OBD dongle	Device to diagnostic socket	more stable data, easy scaling	physical logistics, compatibility, risk of disconnection	Medium fleets where you want fast reliable data
OEM/embedded telematics	Data directly from the vehicle	Quality and accuracy, less hardware	Dependency on brands/models, different APIs	Mixed fleet of newer cars, longer contracts
GPS unit	Separate device in vehicle	Independence from mobile, versatility	installation, servicing of devices	when you also need asset protection / usage monitoring
Video telematics (AI camera)	AI evaluates events and risks	best insight into causes, tailored training	Higher costs, higher demands on GDPR processes	Logistics, high claims, specific risks

Selection criteria that decide:

Accuracy and stability of data (especially for claims),
Ability to explain scores (for both managers and drivers),
reporting for CFO (KPI, trend, savings),
integration (fleet system, HR, insurance processes),
GDPR, data security and retention.

Specific data and "what to track" (Slovakia/EU)

Driver scoring is based on a simple principle: if you can reduce the number of risk events, you reduce the probability of loss.

KPIs that have a direct impact on damages and costs:

Number of hard braking / 100 km,
speeding (both time and intensity),
Driving at risky times (night, rush hour),
phone use (if measured),
number of incidents per 10 000 km,
damage rate (frequency of damage) and average amount of damage,
vehicle downtime after damage (days out of service).

Example of a reasonable target for a pilot (3-4 months):

Reduce hard braking by 10-20% in the risk group,
reduce speeding by 15%,
Demonstrate correlation of scores vs. historical damage.

Business case: where ROI is realistically generated

It's important for CFOs to separate "nice to have" from measurable return.

1) Direct savings

Fewer claims → lower deductibles and uninsured costs,
less downtime → lower replacement vehicle costs,
lower service costs (aggressive driving = faster brake/pneumatic wear).

2) Indirect savings

Better bargaining position with the insurance company (prevention program),
better discipline in the use of vehicles (car policy),
lower reputational and HR risks (employee safety).

3) Investment view

Driver scoring is typically OPEX (SaaS + telematics). It is worth counting on when making an investment assessment:

Cost per vehicle/month (platform + equipment/service),
internal time (fleet, HR, work safety),
one-off integration (if done).

Tip: The fastest payback projects are where you have a higher claims rate, a lot of miles per year or operation in risky modes (logistics, service, frequent moves).

Step-by-step implementation (without chaos)

1) Damage diagnosis and baseline

Divide damage into types (parking, rear-end collision, glass damage, totals),
Calculate annual "cost of risk" (premiums + deductibles + downtime),
identify 3-5 KPIs you want to improve.

2) Pilot (8-16 weeks)

Select a representative group (e.g. 30-100 drivers),
set rules for communication and motivation,
Monitor adoption (app/device activity) and data quality.

3) Scale

Set up "policy + coaching" for top risk takers,
Involve HR/BOH (training, employer responsibilities),
Set up regular reporting for CFO (monthly dashboard).

4) Link to claims process

Ideally link scoring to claims events:

Quick damage reporting,
automatic recording of incidents,
"pre-damage" analysis (behaviour change in the weeks before the event).

Trends 2026: where driver scoring is going

Explainable AI (XAI): push for explainability - to make it clear why a driver has a score.
Hybrid telematics: combination of OEM data + mobile + camera as needed.
Integration into fleet reporting: scoring stops being a "separate app" and becomes part of fleet controlling.
Data security and AI regulation: more processes, audits and emphasis on proper role setup (provider vs user).

Legal and reputational risks: GDPR and AI regulation (practically)

Driver scoring works with data that may be personal (if linked to the driver). Worth addressing in practice:

GDPR minimum checklist

Legal basis for processing (typically legitimate interest; consent is risky for employees),
transparent information to drivers (what we measure, why, for how long),
data minimization (collect only what you need for security and cost),
retention (e.g., 3-12 months for behavioral data, longer for claims data - as appropriate and internal rules dictate),
access rights and logging (who sees the score and why),
Contracts with suppliers (processing contracts, security standards).

AI Act: why the CFO should monitor it

The EU is gradually tightening requirements for AI systems. In practice, this means more emphasis on:

documentation, risk management and data quality,
Explainability of outputs,
auditability and monitoring.

Important: although driver scoring may not always fall into "high-risk" categories, the trend is clear - choose solutions that can demonstrate safe and fair operation of models.

How this fits into AVIS fleet services (MaxiRent / Lease)

In practice, driver scoring works best as part of wider fleet management:

Telematics/GPS as a data source (location, mileage, driving style, idling speed),
fleet reporting (from excel to regular controlling and AI analysis),
integration into corporate IT (unified data, automation, reporting to CFO),
claims reporting process and event logging.

What AVIS can do practically:

Design pilot and KPIs for your fleet,
recommend the appropriate type of telematics (according to models and usage),
set up reporting and regular review for CFO/risk,
help with the claims process (speed, recording, prevention).

Frequently asked questions (FAQ)

1) Is driver scoring "employee tracking"?

Not if it is set up as a safety and cost tool with clear rules, transparency and data minimization. The key is communication and fair use.

2) Which solution is best for a quick start?

Most often smartphone telematics or OBD (depending on the fleet). A quick pilot will show if the data correlates to damage.

3) How quickly will I see the result?

The first behavioral improvements can be seen within 4-8 weeks. For a serious business case we recommend 3-6 months, ideally with a comparison to historical damage.

4) Can this reduce premiums?

Directly depends on the insurer and the portfolio, but a prevention program (data + coaching) is a strong argument in negotiations and in reducing claims.

5) Do I need a camera in my vehicle?

Not always. A camera gives the best context for accidents and coaching, but is more expensive and more challenging for GDPR processes. For many companies, telematics + scoring is enough.

Summary / TL;DR

AI driver scoring turns the "feel" of risk into measurable KPIs and interventions.
The biggest benefit is in damage prevention, downtime and better car policy discipline.
The choice of solution is based on data quality, explainability and integrations.
A pilot of 8-16 weeks is the fastest way to validate ROI.
GDPR and AI regulation are manageable when processes are set up from the beginning.

Keywords and entities (used + related)

Main KW: AI, driver behavior, fleet insurance, AI driver scoring, driver scoring

Related KWs and entities: telematics, GPS, fleet management, fleet reporting, risk management, claims, PZP, collision insurance, usage-based insurance (UBI), machine learning, explainable AI (XAI), video telematics, dashcam AI, GDPR, EU AI Act, OSH, car policy, TCO, downtime, driver coaching.

Conclusion

AI driver scoring is not "just another app" but a concrete risk management tool - with implications for insurance claims, downtime and TCO. To see if it makes sense in your fleet, start with the pilot and set measurable KPIs.

Want a pilot design and KPIs for your business? Get in touch with AVIS - we'll make a recommendation for a solution (telematics, reporting, claims process) based on your fleet type and risk profile.

AI scoring of drivers: a new tool to reduce insurance claims