Which key questions about underwriting proxies should you be asking - and why do they matter?
Most people assume underwriting is purely about individual behaviour. The truth is different: many firms use hard proxies like postcode and job title because they are cheap, widely available and statistically convenient. That creates practical problems for customers and moral problems for society. Below are the questions I will answer, and why each matters to you.
- What exactly are underwriting proxies, and why do firms use postcode and job title? Does using those proxies mean an insurer or lender is intentionally discriminatory? How can you challenge or improve an underwriting decision that seems unfair? Should you favour providers that use alternative data or advanced models? What regulatory or technological changes are coming that will change how underwriting works?
These questions matter because the answers affect price, access and long-term financial health. If a postcode stands between you and affordable car insurance, you need to know why and what you can do about it.
What exactly do underwriters mean when they use postcode and job title as proxies?
Underwriters want to predict risk - who will make a claim, who might default on a loan, who is likely to cancel a policy early. Collecting every detail about every customer would be expensive. So firms use proxies: variables that stand in for the true, often unobservable, risk drivers.
Postcode acts as a proxy for factors such as crime rates, road conditions, local claims history and socio-economic status. Job title stands in for income stability, routine patterns, likely time spent commuting and sometimes occupational hazards. Together these fields are easy to collect and show clear statistical relationships with outcomes in many datasets.
Example: An insurer finds that drivers in Postcode A make twice as many claims as those in Postcode B. Without detailed behavioural data, applying a postcode-based loading is the quickest way to price risk. That is practical but blunt.
Does using postcode or job title amount to deliberate discrimination?
Short answer: usually not intentionally, but the effect can be discriminatory. That distinction is important. Underwriters rarely say "we will penalise people of X group". They say "postcode X correlates with claims, we need to price accordingly". The result may be that protected groups face worse outcomes because postcode and protected characteristics are correlated.
Two scenarios help explain this.
Scenario 1 - Statistical convenience
A lender uses job title to estimate income volatility. A junior nurse and a freelance software tester may both be labelled as "healthcare" or "IT contractor" depending on data sources. The lender's model flags contractors as higher risk, increasing rates or refusing credit. The intention was risk management, not bias, but the contractors lose out.
Scenario 2 - Historical bias baked into data
A postcode historically associated with underinvestment also shows higher default rates because people there had fewer job opportunities. The model learns that postcode predicts bad outcomes and raises prices. Customers who live there face higher costs partly because of decades of policy choices, not current behaviour.
In both cases there is harm, even if unintentional. UK law recognises both direct discrimination and cases where seemingly neutral rules have a disproportionate adverse impact on protected groups. Regulators also expect firms to think about fairness when using data-driven pricing.
How do you challenge or improve an underwriting decision that looks unfair?
If a decision affects you - a declined application, a high premium - you have rights and practical steps to take. Treat this like a consumer dispute: gather evidence, ask questions, and escalate where needed.
Ask for the reasoning. Under the Data Protection Act and FCA rules, you can request meaningful information about automated decisions. Ask which factors contributed and why postcode or job title affected the outcome. Check your data. Request your credit report, check for errors in address history, employment records and any third-party data. Mistakes are common and easy to fix. Provide context. If your job title is a short, misleading label, supply payslips, a contract, or a letter from HR. If your postcode was flagged because of a few local incidents, explain your personal risk profile - security measures for your home or off-street parking for your car. Negotiate. Ask for manual underwriting or a human review. Many firms have discretion to look beyond model output when customers provide additional evidence. Use comparison tools and specialist providers. Some insurers and lenders explicitly refuse to use postcode-based loading, or they use telematics and open banking to price risk by behaviour rather than location. Escalate to regulators or an ombudsman. If you believe a firm has acted unfairly and internal complaint routes fail, the Financial Ombudsman Service and relevant regulatory bodies can investigate.Real example: A young mother in an outer London postcode found her motor insurance quotes double compared with the city centre. She provided a year of telematics data showing safe driving, switched to a provider that used that data, and reduced her premium by 40% within six months. The key was replacing postcode as the dominant predictor with actual behaviour.
Can switching to alternative data or more advanced models actually improve outcomes for customers?
Yes - but there are trade-offs. Alternative data - telematics, open banking, rental payment history and device signals - can create a more personalised and dynamic picture of risk. That often benefits safe, lower-risk people who were penalised by blunt proxies. It also makes pricing fairer in principle.
Advanced models can detect and adjust for proxies too. Here are some practical techniques:
- Proxy detection - identify features that correlate strongly with protected attributes and test how much they drive outcomes. Counterfactual testing - ask whether a small change in a protected attribute would alter the decision. If yes, the model may be unfair. Fairness-aware training - constrain models to balance error rates across groups or to meet equalised odds criteria. Explainability tools - use LIME or SHAP to produce human-readable explanations for individual decisions, making it easier to contest or correct errors.
Example: A fintech lender uses open banking. Instead of using job title, it inspects actual income flow and regular outgoings. Applicants with irregular titles but stable cashflow score better. That widens access to credit among gig-economy workers who would previously be penalised.
But a warning: new data can create new privacy risks and new proxies. Telematics might better reflect driving, yet it could also reveal sensitive lifestyle details. Firms must balance accuracy, fairness and privacy.
Is it better to pick a provider that uses behavioural data rather than postcode and job title?
It depends on your situation. If you have a benign profile but live in a penalised postcode, behavioural data can save you money. If the behavioural data is of poor quality - for instance a short telematics window that coincides with winter driving - it could make outcomes worse.
Questions to ask prospective providers:
- What data do you collect and why? Do you allow opt-outs for specific data sources? Can I see a plain-language explanation for decisions? Do you monitor models for disparate impact?
Case study: Two applicants with similar driving records applied for the same insurer. One lived in a flagged postcode but agreed to telematics; the other refused telematics and kept postcode-rated pricing. The telematics user saved money and improved his future pricing with a clean driving score. Outcomes like that encourage more customers to choose behaviour-based pricing.
What regulatory and technological changes are coming that will change how underwriting uses proxies?
Regulation and tech both push in the direction of transparency and fairness. Expect these trends to grow through 2026 and beyond.
- More scrutiny from the FCA. The Financial Conduct Authority has demanded fair treatment and clear explanations where automation impacts customers. Firms must be able to justify model design choices and monitor outcomes for bias. Data rights under UK GDPR. Individuals can request information about automated decisions and correct errors in their data. While the "right to explanation" is debated, meaningful information about decision-making is increasingly enforced. Standardisation of fairness testing. Industry groups and regulators are encouraging common metrics for detecting disparate impact. That will make it harder for firms to bury unjust proxies inside opaque models. Wider adoption of alternative data. Open banking, connected car data and verified employment APIs will become common. Expect more personalised pricing as a result. Community pressure and litigation. Cases that show postcode-linked discrimination are likely to spur legal action and public scrutiny, leading firms to change practices.
Overall, the balance will tilt towards models that explain themselves and that can be audited for fairness. That will not erase risk-based pricing, but it may reduce blunt postcode and job title loadings.
Self-assessment quiz: Is your situation affected by proxy-based underwriting?
Answer yes or no. Score 1 point for each yes.
Have you been quoted much higher prices than people you know with similar habits? Do you live in an area known for higher claims or higher crime rates? Does your job title look short or generic on application forms? Have you been denied credit without a clear, human explanation? Have you had trouble correcting address or employment errors in third-party data?Scoring guide: 0 - You are probably not being hit by blunt proxies; 1-2 - You may be partly affected and should gather data; 3-5 - Strong chance proxies are shaping outcomes - take the challenge steps above and consider switching providers.
Checklist: Practical next steps you can take today
- Request a plain-language explanation for any adverse automated decision. Pull your credit report and correct any data errors. Compare providers who use telematics or open banking. Supply supporting documents that give context to job title or address. File a complaint with the firm, then escalate to the Financial Ombudsman if needed.
How should you think about the trade-off between fairness, privacy and accuracy?
There is no single correct answer. If you prize privacy, you may prefer the bluntness of postcode pricing because it avoids continuous tracking. If you want the lowest possible price and can share fine-grained data, behavioural models will probably reward you. From a social viewpoint, using behaviour rather than location tends to reduce inequality caused by structural factors.
A reasonable https://www.independent.co.uk/life-style/car-insurance-telematics-black-box-smartphone-b2889050.html personal rule: if a model penalises you for things you can change through your own behaviour, it is more acceptable. If it penalises you for historical factors beyond your control, push for alternatives or challenge the firm.
Final thoughts - what practical shifts will help customers most?
Two changes will make the largest difference for most people. First, wider consumer access to and control of their data - credit files, employment records, banking flows - so mistakes are fixed and accurate behaviour is visible. Second, regulatory pressure that forces firms to test for disparate impact and to offer human review or alternative pricing paths when automated systems disadvantage groups.
Until then, be proactive. Check your data, ask for explanations, and consider providers who price on behaviour. That is the most realistic way to stop your postcode or job title from doing the heavy lifting in ways that are unfair.