Preprocess list data
The ultimate end goal of your modeling is going to affect the way you want to format your data. It's a good practice whenever you start a machine learning project to ask yourself, what is the precise question you want to answer, because whatever model you generate, it's only going to make sense if used in the context of the question asked. 

If in your case you want to predict loan approvals, then first you need to check whether that information is even present in the data you have. The JSON you have just shows historical data, but do you know the outcome for each data point? Is that another feature, maybe recorded in the "someValue", "title" area of the file? Without it, you really can't do anything.

If you can get that information, then it's perfectly fine to generate a variable for each year: assets_year1, assets_year2, ... assets_year8, investments_year1, ... etc. True, year8 might have a lot of Null values, but that's not necessarily bad. For example, most models working with text data consist of really sparse training matrices, yet they do very well in practice. Depending on the algorithm, it might weed out those variables anyways. With Null values, you just have to try imputing the Null values differently, and you can also create an indicator variable for whether the column has missing values, and see how different settings change performance. 

To take it a step further, you can also create additional features to encapsulate the nuances that are occurring over the years. Create features like the historical average, average change per year, total number of years of history, etc. Creative feature engineering is the hardest part, but can lead to big changes in performance.