# Artificial Neural Network for Classifying Financial Performance in Jordanian Insurance Sector

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## Abstract

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## 1. Introduction

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_{04}:## 2. Literature Review

## 3. Methodology and Mathematical Models

#### 3.1. Multilayer Perceptron

#### 3.2. Learning Process

#### 3.3. Resilient Propagation (Rprop)

#### 3.4. Evaluation Measures

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- Precision focuses on positive instances by computing the percentage of TP instances pertaining to true and false positive instances.$$Precision={\displaystyle \frac{TP}{TP+FP}}$$
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- The false positive rate (FP rate) refers to a type I error that associates the percentage of FP to FP and TN metrics.$$FP\phantom{\rule{3.33333pt}{0ex}}rate={\displaystyle \frac{FP}{FP+TN}}=1-TN\phantom{\rule{3.33333pt}{0ex}}rate$$
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- The false negative rate (FN rate) refers to a type II error that associates the percentage of FN to FN and TP metrics.$$FN\phantom{\rule{3.33333pt}{0ex}}rate={\displaystyle \frac{FN}{FN+TP}}=1-TP\phantom{\rule{3.33333pt}{0ex}}rate$$
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- The F-measure (or F-score) is the harmonic mean of precision and sensitivity $\frac{TP}{TP+FN}$.$$-Fmeasure=2\ast {\displaystyle \frac{Precision\ast Sensitivity}{Precision+Sensitivity}}$$
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- Accuracy (ACC) is the proportion of true positive and negative instances over the total number of instances.$$ACC={\displaystyle \frac{TP+TN}{P+N}}={\displaystyle \frac{TP+TN}{TP+TN+FP+FN}}$$

## 4. Empirical Setting and Results

#### 4.1. Data Description

#### 4.2. Multicollinearity Tests

#### 4.3. Multiple Regression Models

#### 4.4. MLP Model

## 5. Discussion

## 6. Limitations and Directions for Future Research

## 7. Conclusions

## Author Contributions

## Funding

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Note

1 | History of Insurance Regulations and Development of Regulations in Jordan. Jordan Insurance Federation, Retrieved from http://www.joif.org/ (accessed on 1 December 2022). |

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Variables | Size | Mean | Std. Deviation | Minimum | Maximum |
---|---|---|---|---|---|

TAT | 195 | 0.63604 | 0.18747 | 0.30543 | 1.16872 |

Subrogation (SB) | 195 | 1,601,135 | 1,210,207 | 0.000000 | 5,237,623 |

Claims Paid (CP) | 195 | 17,496,882 | 15,278,153 | 2,260,277 | 93,965,336 |

Market Capitalization (MC) | 195 | 17,901,864 | 17,295,558 | 2,100,000 | 89,400,000 |

Total Shareholders’ Equity (SE) | 195 | 15,431,180 | 11,107,521 | 2,642,703 | 48,140,795 |

Tolerance | VIF | |
---|---|---|

Ln(SB) | 0.512 | 1.953 |

Ln(CP) | 0.418 | 2.391 |

Ln(MC) | 0.298 | 3.356 |

Ln(ES) | 0.269 | 3.718 |

Independent Variables | OLS | Fixed Effect | Random Effect | ||||||
---|---|---|---|---|---|---|---|---|---|

Estimate | Std. Error | T-Stat | Estimate | Std. Error | T-Stat | Estimate | Std. Error | T-Stat | |

Intercept | −0.605 | 0.247 | −2.447 ** | −0.133 | 0.278 | −0.480 *** | |||

Ln(SB) | 0.042 | 0.013 | 3.138 *** | 0.043 | 0.014 | 3.148 *** | 0.044 | 0.013 | 3.293 *** |

Ln(CP) | 0.204 | 0.021 | 9.883 *** | 0.133 | 0.021 | 6.394 *** | 0.155 | 0.021 | 7.539 *** |

Ln(MC) | 0.047 | 0.020 | 2.352 ** | 0.081 | 0.026 | 3.169 *** | 0.065 | 0.023 | 2.870 *** |

Ln(SE) | −0.211 | 0.025 | −8.506 *** | −0.215 | 0.027 | −7.843 *** | −0.212 | 0.026 | −8.060 *** |

R-squared/Adjusted R-squared | 0.590/0.581 | 0.462/0.414 | 0.498/0.487 | ||||||

F-stat. | 68.36 *** | 38.251 *** | 188.409 *** |

Iterations | Sample | Matrix | TP Rate | TN Rate | FP Rate | FN Rate | ||
---|---|---|---|---|---|---|---|---|

Low | High | |||||||

n = 500 | Train (80%) | Low | 25 | 19 | 0.532 | 0.826 | 0.174 | 0.468 |

High | 22 | 90 | ||||||

Test (20%) | Low | 5 | 5 | 0.455 | 0.821 | 0.179 | 0.545 | |

High | 6 | 23 | ||||||

n = 1000 | Train (80%) | Low | 34 | 10 | 0.642 | 0.903 | 0.097 | 0.358 |

High | 19 | 93 | ||||||

Test (20%) | Low | 7 | 3 | 0.636 | 0.893 | 0.107 | 0.364 | |

High | 4 | 25 | ||||||

n = 1500 | Train (80%) | Low | 33 | 11 | 0.647 | 0.895 | 0.105 | 0.353 |

High | 18 | 94 | ||||||

Test (20%) | Low | 7 | 3 | 0.636 | 0.893 | 0.107 | 0.364 | |

High | 4 | 25 | ||||||

n = 10,000 | Train (80%) | Low | 35 | 9 | 0.778 | 0.919 | 0.081 | 0.222 |

High | 10 | 102 | ||||||

Test (20%) | Low | 7 | 3 | 0.700 | 0.897 | 0.103 | 0.300 | |

High | 3 | 26 |

Iterations | Sample | ACC | Precision | F-Score |
---|---|---|---|---|

n = 500 | Train (80%) | 0.737 | 0.568 | 0.549 |

Test (20%) | 0.718 | 0.500 | 0.476 | |

n = 1000 | Train (80%) | 0.814 | 0.773 | 0.701 |

Test (20%) | 0.821 | 0.700 | 0.667 | |

n = 1500 | Train (80%) | 0.814 | 0.750 | 0.695 |

Test (20%) | 0.821 | 0.700 | 0.667 | |

n = 10,000 | Train (80%) | 0.878 | 0.795 | 0.787 |

Test (20%) | 0.846 | 0.700 | 0.700 |

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## Share and Cite

**MDPI and ACS Style**

Al Omari, R.; Alkhawaldeh, R.S.; Jaber, J.J. Artificial Neural Network for Classifying Financial Performance in Jordanian Insurance Sector. *Economies* **2023**, *11*, 106.
https://doi.org/10.3390/economies11040106

**AMA Style**

Al Omari R, Alkhawaldeh RS, Jaber JJ. Artificial Neural Network for Classifying Financial Performance in Jordanian Insurance Sector. *Economies*. 2023; 11(4):106.
https://doi.org/10.3390/economies11040106

**Chicago/Turabian Style**

Al Omari, Rania, Rami S. Alkhawaldeh, and Jamil J. Jaber. 2023. "Artificial Neural Network for Classifying Financial Performance in Jordanian Insurance Sector" *Economies* 11, no. 4: 106.
https://doi.org/10.3390/economies11040106