Reliability has to with being able to replicate results. So if you apply the same measurment technique multiple times in similar situations you should get similar results. An unreliable measurement adds a lot of random noise to your measurement.
Validity has to do with measuring what you want to measure. This is a much more theoretical concept: if this is a survey you just need to think about what the questions are, how a respondent might interpret that question (and the possible answer categories), the theoretical concept you want to measure, and whether these all match up.
Also see here and here
Both convergent and concurrent validity evaluate the association, or correlation, between test scores and another variable which represents your target construct. Here is the difference:
Concurrent validity tests the ability of your test to predict a given behavior. For instance, verifying whether a physical activity questionnaire predicts the actual frequency with which someone goes to the gym. You could administer the test to people who exercise every day, some days a week, and never, and check if the scores on the questionnaire differ between groups.
Convergent validity examines the correlation between your test and another validated instrument which is known to assess the construct of interest. In this case, you could verify whether scores on a new physical activity questionnaire correlate to scores on an existing physical activity questionnaire.
Here is an article which looked at both types of validity for a questionnaire, and can be used as an example: https://www.hindawi.com/journals/isrn/2013/529645/ [Godwin, M., Pike, A., Bethune, C., Kirby, A., & Pike, A. (2013). Concurrent and Convergent Validity of the Simple Lifestyle Indicator Questionnaire. ISRN Family Medicine, 2013, 1–6. https://doi.org/10.5402/2013/529645]
A book by Sherman et al. (2011) has a chapter which describes the types of validity you mention - which are also part of the 'tripartite model of validity.' You may be able to find a copy here https://www.researchgate.net/publication/251169022_Reliability_and_Validity_in_Neuropsychology
The reference for the chapter is
[Sherman, E. M. S., Brooks, B. L., Iverson, G. L., Slick, D. J., & Strauss, E. (2011). Reliability and Validity in Neuropsychology. In The Little Black Book of Neuropsychology (pp. 873–892). Springer US. https://doi.org/10.1007/978-0-387-76978-3_30]
Best Answer
A reliable result that has low validity can't really be redeemed. In your example, it would be like recording something silly like people's height to determine how much money they spent instead of just recording how much money they spent.
An unreliable result that has high validity can be redeemed because it's a problem of measurement error. In your example, suppose you made a numerical mistake in converting currency. In less silly examples, you can integrate measurement error in your model.