PERAMALAN EXPECTED CREDIT LOSS (ECL) MENGGUNAKAN MODEL ARIMA: STUDI KASUS PADA BANK BRI DAN BANK BCA

Suci Dwilianti Tolla; Vicky Vendy

doi:10.35906/equili.v14i2.2528

PERAMALAN EXPECTED CREDIT LOSS (ECL) MENGGUNAKAN MODEL ARIMA: STUDI KASUS PADA BANK BRI DAN BANK BCA

Suci Dwilianti Tolla, Vicky Vendy

Abstract

ABSTRAK

Implementasi PSAK 109 menuntut lembaga perbankan untuk menerapkan pendekatan forward-looking dalam pengukuran cadangan kerugian kredit melalui estimasi expected credit loss (ECL). Jika tidak diterapkan, bank berisiko menghadapi ketidakpatuhan regulasi, berkurangnya transparansi, serta menurunnya kepercayaan publik. Penelitian ini bertujuan untuk memperkirakan nilai ECL menggunakan model autoregressive integrated moving average (ARIMA) pada dua bank besar di indonesia, yaitu bank BRI dan bank BCA, selama periode 2004 hingga 2024. Penelitian menggunakan pendekatan kuantitatif dengan metode purposive sampling dan data sekunder berupa laporan keuangan tahunan yang memuat komponen probability of default (PD), loss given default (LGD), dan exposure at default (EAD). Model ARIMA ditentukan melalui uji stasioneritas ADF (Augmented Dickey-Fuller Test) dan KPSS (Kwiatkowski-Phillips-Schmidt-Shin Test), serta identifikasi parameter menggunakan grafik ACF (Autocorrelation Function) dan PACF (Partial Autocorrelation Function). Pemilihan model terbaik dilakukan berdasarkan nilai AIC (Akaike Information Criterion) dan BIC (Bayesian Information Criterion). Hasil penelitian menunjukkan bahwa baik Bank BRI maupun Bank BCA paling sesuai dimodelkan dengan ARIMA yang mencerminkan kesamaan karakteristik statistik pada data historis ECL kedua bank. Model yang diperoleh mampu memproyeksikan tren ECL dengan cukup akurat untuk periode 2025 hingga 2029. Penelitian ini diharapkan dapat menjadi acuan dalam penyusunan kebijakan pencadangan kerugian kredit yang lebih adaptif dan berbasis data historis.

ABSTRACT

The implementation of PSAK 109 requires banks to adopt a forward-looking approach in measuring credit loss allowances through Expected Credit Loss (ECL) estimation. Without this standard, banks may face regulatory non-compliance, reduced transparency, and declining public trust. This study estimates ECL using the Autoregressive Integrated Moving Average (ARIMA) model for Bank BRI and Bank BCA during 2004–2024. A quantitative approach with purposive sampling was applied, using annual financial statements containing Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). Model selection involved stationarity tests with the Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests, parameter identification through Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF), and evaluation based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The results show that both banks are best represented by ARIMA indicating similar statistical patterns in their historical ECL data. These findings are expected to support the development of more adaptive and data-driven credit loss provisioning policies.

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