Let me know of any other methods to generate response variable when there is none
Anomaly detection techniques could be what you're looking for (but you don't "generate response variable when there is none").
If you can assume that for the available dataset ($m$ examples, $n$ features):
$$x^{(1)}, \dots x^{(m)} \in \mathbb{R}^{n}$$
the features are gaussian:
$$x_i \sim N(\mu_i, \sigma_i^2)$$
you can compute a Density Estimation ($p(x)$):
$$p(x) = \prod_{j=1}^n p(x_j; \mu_j, \sigma_j^2)$$
where
$$p(x_j; \mu_j, \sigma_j^2) = \frac{1}{\sqrt{2 \pi} \sigma_j} e^{-\frac{(x - \sigma)^2}{2 \sigma^2}}$$
(this is the formula for independent features but, in practice, it works quite well even if the features are not independent)
and classify a claim as being fraudulent when $p(x) < \epsilon$.

The problem is that you need a good $\epsilon$ (threshold value).
You can split your data in:
- a training set. Ideally this shouldn't contain anomalous (fraudulent) data points, but if a few of them are present it isn't a big problem.
- a cross validation set. This should contain some known anomalous elements.
As you can see this is not unsupervised anomaly detection: you assume that the available data are legit and you add some examples of anomalous data for the cross validation set (of course you can also examine carefully the data and mark just a few examples).
The key points are:
- for a random sample of insurance records a good share is probably legit (this is the reason that anomaly detection is frequently used for fraud detection);
- you just need a very small number of examples which are known to be fraudulent (to be placed into the cross training set).
The training set is used to compute $\mu_i$, $\sigma_i$ and thus the density estimation.
The "optimal" value of $\epsilon$ is the value that maximizes some evaluation metrics on the cross validation set. A simple evaluation metric is F1-score (data is very skewed and accuracy is not good).
This method can be adapted to non-gaussian features and features that have have some correlation with each other.
For a good introduction and further details take a look at week 7 of Andrew Ng's Machine Learning Class (and to Alex's notes).
There are also completely unsupervised anomaly detection techniques (they make the implicit assumption that normal instances are far more frequent than anomalies): k-NN Global Anomaly Score, Local outlier factor... (see Anomaly Detection : A Survey by Varun Chandola, Arindam Banerjee, Vipin Kumar).
Anyway for your kind of problem semi-supervised anomaly detection seems more promising.