Update README.md

README.md

@@ -1,11 +1,12 @@
-# Loss optimising loan recovery decision times using forecast cash flows
-A theoretical method is empirically illustrated in finding the best time to forsake a loan such that the overall credit loss is minimised. This is predicated by forecasting the future cash flows of a loan portfolio up to the contractual term, as a remedy to the inherent right-censoring of real-world `incomplete' portfolios. Two techniques, a simple probabilistic model as well as an eight-state Markov chain, are used to forecast these cash flows independently. In devising a comparative experimental framework, both techniques are parametrised from different segments within residential mortgage data prior forecasting, which is provided by a large South African bank. As a result, the recovery decision's implied timing is empirically illustrated as a multi-period optimisation problem across uncertain cash flows and competing costs. Using a delinquency measure as a central criterion, the procedure helps find a loss-optimal threshold at which loan recovery should ideally occur for a given portfolio. Furthermore, both the portfolio's historical risk profile and forecast thereof are shown to influence the timing of the recovery decision. This work can therefore facilitate the revision of relevant bank policies or strategies towards optimising the loan collections process, especially that of secured lending.
+# The loss optimisation of loan recovery decision times using forecast cash flows
+
+A theoretical method is empirically illustrated in finding the best time to forsake a loan such that the overall credit loss is minimised. This is predicated by forecasting the future cash flows of a loan portfolio up to the contractual term, as a remedy to the inherent right-censoring of real-world `incomplete' portfolios. Two techniques, a simple probabilistic model as well as an eight-state Markov chain, are used to forecast these cash flows independently. In devising a comparative experimental framework, both techniques are parametrised from different segments within residential mortgage data, which is provided by a large South African bank. As a result, the recovery decision's implied timing is empirically illustrated as a multi-period optimisation problem across uncertain cash flows and competing costs. Using a delinquency measure as a central criterion, the procedure helps to find a loss-optimal threshold at which loan recovery should ideally occur for a given portfolio. Furthermore, both the portfolio's historical risk profile and forecasting thereof are shown to influence the timing of the recovery decision. This work can therefore facilitate the revision of relevant bank policies or strategies towards optimising the loan collections process, especially that of secured lending.
 
 ## Structure
-This R-codebase can be run sequentially using the file numbering itself as a structure. Delinquency measures are algorithmically defined in **DelinqM.R** as data-driven functions, which may be valuable to the practitioner outside of the study's current scope. Note that scripts 3.2 and 3.3 are interactive scripts wherein the loss optimisation procedure is repeatedly run by rerunning the script with different settings, as set out in the comments. Each independent run produces results that are saved for graphing later on.
+This R-codebase can be run sequentially using the file numbering itself as a structure. Delinquency measures are algorithmically defined in **DelinqM.R** as data-driven functions, which may be valuable to the practitioner outside of the study's current scope. Note that scripts 3.2 and 3.3 are interactive scripts wherein the so-called Loss-based Recovery Optimasation across Delinquency (or LROD) procedure is repeatedly run by executing the script with different settings, as set out in the comments. Each independent run produces results that are saved for graphing later on (see the graph-varients of each numbered script where relevant).
 
 ## Data
-This R-codebase assumes that monthly loan performance data are available. Naturally, the data itself can't be made publically available due to non-disclosure agreements signed with the bank in question. However, the structure and type of data that is required for reproducing this study, is sufficiently described in the commentary within the scripts. This should enable the practitioner to extract and prepare data accordingly.
+This R-codebase assumes that monthly loan performance data are available. Naturally, the data itself can't be made publically available due to non-disclosure agreements signed with the particular bank in question. However, the structure and type of data that is required for reproducing this study, is sufficiently described in the commentary within the scripts. This should enable the practitioner to extract and prepare data accordingly.
 
 ## Copyright
-All code and scripts are hereby released under an [MIT](https://opensource.org/licenses/MIT) license. Similarly, all graphs produced by relevant scripts as well as those published here, are hereby released under a Creative Commons Attribution ([CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/)) licence.
+All code and scripts are hereby released under an [MIT](https://opensource.org/licenses/MIT) license. Similarly, all graphs produced by relevant scripts as well as those published here, are hereby released under a Creative Commons Attribution ([CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/)) licence.