Financial fraud is escalating as financial services and operations grow. Despite preventive
actions and security measures deployed to mitigate financial fraud, fraudsters are learning …

We compare the performance of a wide set of regression techniques and machine-learning
algorithms for predicting recovery rates on non-performing loans, using a private database …

A novel procedure is presented for the objective comparison and evaluation of a bank's
decision rules in optimising the timing of loan recovery. This procedure is based on finding a …

In this article, we give various recommendations to boost the performance of credit risk
models. It is based upon more than two decades of research and consulting on the topic …

Even though sovereign bonds represent low-risk alternatives that give investors a healthy
income, the risk assessment process for these bonds is still considered subjective because …

Managing the collection of unpaid debts is crucial for the financial survival of the companies.
The long term unpaid debts are collected through legal debt collection processes. This legal …

A new Bayesian informative prior specification method (BAF method–Bayesian priors using
ARIMA forecasts) is proposed to introduce additional information into credit risk modelling …

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 …

The new book" Smart Green Energy Production" explores the innovative surfaces and
Intersections between Intelligent Algorithms and Green Energy Technologies to advance …

The state-of-the-art in forecasting debt recovery from portfolios of non-performing unsecured
consumer loans is to use stochastic models of payment behaviour of individual customers …