Paradoks Algoritma Dalam Arsitektur Finansial: Menguji Penetrasi Kognitif AI dan Efisiensi Biaya Operasional Terhadap Profitabilitas Perbankan Berbasis Mitigasi Risiko Kredit

Andre Purnama

doi:10.55338/jeama.v5i1.1.1156

Authors

Andre Purnama Universitas Riau

DOI:

https://doi.org/10.55338/jeama.v5i1.1.1156

Keywords:

Penetrasi kognitif AI, Efisiensi Biaya Operasional, Mitigasi Risiko Kredit, Profitabilitas Perbankan , PLS-SEM

Abstract

Adopsi kecerdasan buatan (AI) dan tekanan efisiensi biaya telah mengubah operasional perbankan nasional, namun bukti empiris mengenai efektivitasnya dalam memitigasi risiko kredit sekaligus mendorong profitabilitas masih terbatas, khususnya pada era pascapandemi dan ledakan bank digital di Indonesia. Penelitian ini menguji pengaruh penetrasi kognitif AI dan efisiensi biaya operasional terhadap profitabilitas perbankan, baik secara langsung maupun melalui mitigasi risiko kredit sebagai mediator. Data sekunder panel dari 30 bank umum di Indonesia periode 2021-2025, mencakup bank BUMN, swasta nasional, pembangunan daerah, dan bank digital murni yang dianalisis menggunakan Partial Least Squares Structural Equation Modeling (PLS-SEM). Penetrasi kognitif AI diukur melalui indeks komposit content analysis laporan tahunan, efisiensi biaya melalui Cost to Income Ratio, mitigasi risiko kredit melalui Non-Performing Loan gross, dan profitabilitas melalui Return on Assets. Hasil menunjukkan penetrasi kognitif AI dan efisiensi biaya operasional berpengaruh signifikan terhadap profitabilitas secara langsung, dan mitigasi risiko kredit juga berpengaruh signifikan terhadap profitabilitas, tetapi keduanya tidak berpengaruh signifikan terhadap mitigasi risiko kredit, sehingga peran mediasinya tidak terbukti. Temuan ini mengindikasikan paradoks algoritma: kontribusi teknologi dan efisiensi biaya terhadap kinerja finansial belum diimbangi penguatan proporsional fungsi manajemen risiko kredit. Penelitian ini memberi implikasi teoretis bagi pengembangan Resource-Based View dan Dynamic Capability dalam digitalisasi perbankan, serta implikasi praktis bagi manajemen bank dan regulator dalam merancang kebijakan integrasi teknologi yang selaras dengan penguatan manajemen risiko kredit.

References

[1] Balnk Indonesial. (2024). Bluleprint Sistem Pembalyalraln Indonesial 2025-2030. Jalkalrtal: Balnk Indonesial.

[2] Sebalyalng, T. E., Halkim, D. B., Balkhtialr, T., & Indralwaln, D. (2024). “Whalt drives mobile balnking aldoption dule to Covid-19 oultbrealks? ALn empiricall investigaltion of the youlnger generaltion in Indonesial”. Procedial Compulter Science, 234, 1192–1203. https://doi.org/10.1016/j.procs.2024.03.115

[3] ULlal, V. R. (2025). “Digitall tralnsformaltion in the Indonesialn balnking indulstry: al qulallitaltive SWOT alnallysis of mobile balnking initialtives in conventionall finalnciall institultions”. Joulrnall of Balnks alnd Finalnciall Institultions, 1(2), 85–99.

[4] Otoritals Jalsal Keulalngaln. (2024). Staltistik Perbalnkaln Indonesial Desember 2024. Jalkalrtal: OJK.

[5] Swalnkie, G. D. B., & Broby, D. (2019). “Exalmining the impalct of alrtificiall intelligence on the evallulaltion of balnking risk”. Centre for Finalnciall Regullaltion alnd Innovaltion, ULniversity of Stralthclyde.

[6] Demirel, S., & Topcul, M. (2024). “The impalct of alrtificiall intelligence alpplicaltions on digitall balnking in Tulrkish balnking indulstry”. ALdvalnces in Hulmaln-Compulter Interalction, 2024, 9921363. https://doi.org/10.1155/2024/9921363

[7] ALtaldogal, AL., Obi, O. C., Onwulsinkwule, S., Dalwodul, S. O., Osalsonal, F., & Dalralojimbal, AL. I. (2024). “ALI's evolving impalct in ULS balnking: aln insightfull review”. Internaltionall Joulrnall of Science alnd Resealrch ALrchive, 11(1), 904–922.

[8] Elshalmly, AL. B., Rehmaln, S. UL., Ralhmaln, M. M., Halmeed, R., & Jalmeel, Z. (2023). “ALI-balsed green balnking technologies alnd balnk stalbility – moderalting role of climalte chalnge”. Joulrnall of Nalmibialn Stuldies: History Politics Culltulre, 35, 217–228.

[9] Kikulchi, T. (2026). “The innovaltion talx: generaltive ALI aldoption, produlctivity palraldox, alnd systemic risk in the UL.S”. balnking sector. alrXiv preprint, alrXiv:2602.02607.

[10] Bi, S., & Balo, W. (2024). “Innovaltive alpplicaltion of alrtificiall intelligence technology in balnk credit risk malnalgement”. Internaltionall Joulrnall of Globall Economics alnd Malnalgement, 2(3), 62–70. https://doi.org/10.62051/IJGEM.v2n3.08

[11] Edulnjobi, T. E., & Odejide, O. AL. (2024). “Theoreticall fralmeworks in ALI for credit risk alssessment: towalrds balnking efficiency alnd alcculralcy”. Internaltionall Joulrnall of Scientific Resealrch ULpdaltes, 7(1), 92–102. https://doi.org/10.53430/ijsrul.2024.7.1.0030

[12] ALl-ALfifi, AL. AL. M. (2024). “The impalct of ulsing alrtificiall intelligence alpplicaltions on redulcing the defalullt risk in MSMEs”. EulroMid Joulrnall of Bulsiness alnd Tech-Innovaltion (EJBTI), 2(1), 1–14.

[13] Valn Thiel, D., & Valn Ralalij, W. F. F. (2019). “ALrtificiall intelligence credit risk prediction: aln empiricall stuldy of alnallyticall alrtificiall intelligence tools for credit risk prediction in al digitall eral”. Joulrnall of Risk Malnalgement in Finalnciall Institultions, 12(3), 268–286.

[14] Ikuldalbo, AL. O., & Kulmalr, P. (2024). “ALI-driven risk alssessment alnd malnalgement in balnking: ballalncing innovaltion alnd seculrity”. Internaltionall Joulrnall of Resealrch Pulblicaltion alnd Reviews, 5(10), 3573–3588.

[15] ALnestialwalti, C. AL., ALmalndal, C., Khalntinyalno, H., & ALgalthal, AL. (2025). “Balnk FinTech alnd credit risk: compalrison of selected emerging alnd developed coulntries”. Stuldies in Economics alnd Finalnce, 43(2), 281–309. https://doi.org/10.1108/SEF-12-2023-0714

[16] Iralwaln, P., Dalmalyalnti, E., Praltalmal, R. P., Sialgialn, L. D., & Halnggraleni, D. (2025). “Operaltionall risk alnd balnk profitalbility: alnallyzing BOPO alnd efficiency raltios in Indonesialn commerciall balnks”. Julrnall Pendidikaln Indonesial, 6(1), 338–346. https://doi.org/10.59141/jalpendi.v6i1.6775

[17] ALprialni, E. S., Pultri, S. E., & Ralmli, R. (2023). “The effect of credit risk, liqulidity risk alnd operaltionall risk to profitalbility in conventionall balnks listed on Indonesial Stock Exchalnge period 2019–2021”. MEC-J (Malnalgement alnd Economics Joulrnall), 7(1), 63–76. https://doi.org/10.18860/mec-j.v7i1.20512

[18] ALdrialn, F. (2025). “ALnallysis of balnk efficiency in Indonesial balsed on finalnciall raltios”. Edulvest–Joulrnall of ULniversall Stuldies, 5(8), 10481–10499. https://doi.org/10.59188/edulvest.v5i8.51270

[19] Kulsulmal, V., & Buldhidhalrmal, V. (2025). “Exploring proxies of balnk profitalbility in Indonesial from 2012 to 2023”. Julrnall Ilmialh Mulltidisiplin Indonesial, 4(4), 140–152.

[20] ALl-Shalrkals, AL. AL., & ALl-Shalrkals, T. AL. (2022). “The impalct on balnk profitalbility: testing for calpitall aldequlalcy raltio, cost-income raltio alnd non-performing loalns in emerging malrkets”. Joulrnall of Governalnce alnd Regullaltion, 11(1), 231–243. https://doi.org/10.22495/jgrv11i1sialrt4

[21] ALyinulolal, T. F., & Gulmel, B. I. (2023). “The impalct of cost-to-income raltio on balnk performalnce in Nigerial”. Internaltionall Joulrnall of Mulltidisciplinalry alnd Culrrent Edulcaltionall Resealrch, 5(2), 125–137.

[22] ULllalh, S., Maljeed, AL., & Popp, J. (2023). “Determinalnts of balnk's efficiency in aln emerging economy: al daltal envelopment alnallysis alpproalch”. PLoS ONE, 18(3), e0281663.

[23] Dsoulzal, S., Ralbbalni, M. R., Halwalldalr, I. T., & Jalin, AL. K. (2022). “Impalct of balnk efficiency on the profitalbility of the balnks in Indial: aln empiricall alnallysis ulsing palnel daltal alpproalch”. Internaltionall Joulrnall of Finalnciall Stuldies, 10(4), 93. https://doi.org/10.3390/ijfs10040093

[24] Siswalnti, I., Riyaldh, H. AL., Nalwalngsalri, L. C., Yulsoff, Y. M., & Wibowo, M. W. (2024). “The impalct of digitall tralnsformaltion for sulstalinalble bulsiness: the medialting role of corporalte governalnce alnd finalnciall performalnce”. Cogent Bulsiness alnd Malnalgement, 11(1), 2316954. https://doi.org/10.1080/23311975.2024.2316954

[25] Salfitri, AL., & Malchmulddalh, Z. (2025). “Operalting expenses operalting income impalct on finalnciall performalnce conventionall balnks in Indonesial”. Joulrnall of ALpplied ALccoulnting alnd Talxaltion, 10(1), 161–167. https://doi.org/10.30871/jalalt.v10i1.8938

[26] Setyalningsih, AL., Malftulkhin, & Ernitalwalti, Y. (2023). “Pengalrulh bialyal operalsionall pendalpaltaln operalsionall (BOPO), loaln to deposit raltio (LDR), daln net interest malrgin (NIM) terhaldalp retulrn on alsset (ROAL)”. ALT-TALWALSSULTH: Julrnall Ekonomi Islalm, 8(1), 1–19.

[27] Mullyalwisdalwalti, R. AL., Khoiriyalh, L., Inalyalti, AL. AL., & Imtihalnalh, AL. N. (2025). “The impalct of finalnciall raltio indicaltors on profitalbility in shalrial balnks in Indonesial period 2019–2023”. Ekonomical Shalrial: Julrnall Pemikiraln daln Pengembalngaln Ekonomi Syalrialh, 11(1), 133–156. https://doi.org/10.36908/eshal.v11i1.1572

[28] ALkbalry, N. M. M., Trinulgroho, I., Risfalndy, T., & Palmulngkals, P. (2025). “Digitall tralnsformaltion alnd efficiency: evidence from Indonesialn balnks”. Internaltionall Joulrnall of Bulsiness alnd Society, 26(1), 135–150. https://doi.org/10.33736/ijbs.9555.2025

[29] Shen, C., Wul, J., Li, Y., & Chen, Q. (2025). “Does digitall tralnsformaltion improve the cost efficiency of commerciall balnks? Evidence from Chinal”. Finalnce Resealrch Letters, 73, 106619. https://doi.org/10.1016/j.frl.2024.106619

[30] Chalo, N., Zhoul, Y., & Yalng, H. (2024). “How does digitall tralnsformaltion alffect the profitalbility of rulrall commerciall balnks?”. Heliyon, 10(8), e29412. https://doi.org/10.1016/j.heliyon.2024.e29412

[31] Liul, J., & Galo, P. (2024). “Mealsulring corporalte digitall tralnsformaltion: methodology, indicaltors alnd alpplicaltions”. Sulstalinalbility, 16(10), 4087. https://doi.org/10.3390/sul16104087

[32] Salhal, T., Mialh, R., Ralhmaln, M., Dals, S. K., & Hoqule, E. (2023). “Disclosulres for slalvery alccoulnting concerning SDG 8 alnd corporalte alttribultes: al stuldy on the balnking indulstry of Balnglaldesh”. Fultulre Bulsiness Joulrnall, 9(1), 20. https://doi.org/10.1186/s43093-023-00199-z

[33] Otoritals Jalsal Keulalngaln. (2016). Peraltulraln Otoritals Jalsal Keulalngaln Nomor 4/POJK.03/2016 tentalng Penilalialn Tingkalt Kesehaltaln Balnk ULmulm. Jalkalrtal: OJK.

[34] Krippendorff, K. (2013). Content ALnallysis: ALn Introdulction to Its Methodology (3rd ed.). Thoulsalnd Oalks, CAL: Salge Pulblicaltions.

[35] Dialmalntopoullos, AL., & Winklhofer, H. M. (2001). “Index construlction with formaltive indicaltors: aln allternaltive to scalle development”. Joulrnall of Malrketing Resealrch, 38(2), 269–277. https://doi.org/10.1509/jmkr.38.2.269.18845

[36] Halir, J. F., Risher, J. J., Salrstedt, M., & Ringle, C. M. (2019). “When to ulse alnd how to report the resullts of PLS-SEM”. Eulropealn Bulsiness Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

[37] Halir, J. F., Hullt, G. T. M., Ringle, C. M., & Salrstedt, M. (2022). “AL Primer on Palrtiall Lealst Squlalres Strulctulrall Equlaltion Modeling (PLS-SEM) (3rd ed.)”. Thoulsalnd Oalks, CAL: Salge Pulblicaltions.

[38] Becker, J.-M., Ringle, C. M., Salrstedt, M., & Völckner, F. (2015). “How collinealrity alffects mixtulre regression resullts”. Malrketing Letters, 26(4), 643–659. https://doi.org/10.1007/s11002-014-9299-9

[39] Shmuleli, G., Raly, S., Velalsqulez Estraldal, J. M., & Chaltlal, S. B. (2019). “The elephalnt in the room: predictive performalnce of PLS models”. Joulrnall of Bulsiness Resealrch, 100, 508–514.

[40] Cohen, J. (1988). Staltisticall Power ALnallysis for the Behalviorall Sciences (2nd ed.). Hillsdalle, NJ: Lalwrence Erlbalulm ALssocialtes.

[41] Teece, D. J. (2007). “Explicalting dynalmic calpalbilities: the naltulre alnd microfoulndaltions of (sulstalinalble) enterprise performalnce”. Straltegic Malnalgement Joulrnall, 28(13), 1319–1350.

[42] Wernerfelt, B. (1984). “AL resoulrce-balsed view of the firm”. Straltegic Malnalgement Joulrnall, 5(2), 171–180.