BAGIAN / CHAPTER 2
BAGIAN / CHAPTER 2
to enhance customer acquisition. Financial sector innovations are moving towards business models that leverage Artificial Intelligence (AI) to accelerate the mapping of customer needs and the utilization of blockchain to expedite atomic transactions. In accommodating the rapid pace of innovation in the financial sector, OJK continuously strives to balance velocity and quality.
Innovative credit scoring has the potential to significantly impact financial inclusion by reducing barriers to loan acquisition for Micro, Small, and Medium Enterprises (MSMEs) and streamlining credit underwriting processes for banks. While the utilization of alternative data for credit scoring offers several advantages to lenders, it also presents certain challenges. On the one hand, alternative data can provide deeper insights into an MSME's creditworthiness, enabling banks to make more informed lending decisions. Additionally, the reliability of third- party data compared to potentially manipulated financial data can mitigate lending risks. On the challenge side, alternative data is often unstructured and making it more complex to process than traditional financial data. Ensuring the quality of alternative data is paramount for developing robust risk assessment models. Furthermore, the potential for data and model bias to influence lending decisions is a critical concern. The rapid proliferation of innovative credit scoring methodologies, including those employing artificial intelligence without adequate regulatory oversight, poses significant risks of systemic instability, data breaches, and discriminatory lending practices. In response to these concerns, OJK released a Guideline for Responsible and Trustworthy Artificial Intelligence in the Fintech Industry in November 2023. This guidance aims to mitigate risks and promote ethical AI adoption within the sector. By fostering responsible innovation and safeguarding consumer interests, these measures seek to unlock the full potential of alternative data in credit scoring.
ICS berpotensi memberikan dampak yang signifikan terhadap inklusi keuangan dengan mengurangi hambatan bagi Usaha Mikro, Kecil, dan Menengah (UMKM) untuk memperoleh pinjaman dan merampingkan proses penilaian kredit oleh bank. Meskipun pemanfaatan data alternatif untuk penilaian kredit menawarkan beberapa keuntungan bagi pemberi pinjaman, pendekatan ini juga menghadirkan beberapa tantangan. Di satu sisi, data alternatif dapat memberikan pemahaman yang lebih mendalam mengenai kelayakan kredit UMKM, sehingga memungkinkan bank untuk membuat keputusan pemberian kredit yang lebih tepat. Selain itu, keandalan data pihak ketiga dapat memitigasi risiko pinjaman dibandingkan dengan data keuangan yang berpotensi dimanipulasi. Dari sisi tantangan, data alternatif sering kali tidak terstruktur dan mengakibatkan pemrosesan yang lebih rumit dibandingkan data keuangan. Selain itu, memastikan kualitas dari data alternatif sangat penting untuk mengembangkan model penilaian risiko yang robust . Potensi data dan model yang bias juga harus menjadi perhatian yang penting karena dapat mempengaruhi keputusan pemberian kredit. Perkembangan penilaian kredit yang inovatif, termasuk pemanfaatan kecerdasan buatan tanpa pengawasan regulasi yang memadai, dapat menimbulkan risiko ketidakstabilan sistemik yang signifikan, metodologi pencurian data, dan pemberian pinjaman yang diskriminatif. Menanggapi pemrasalahan tersebut, OJK merilis Pedoman Kecerdasan Buatan yang Bertanggung Jawab dan Terpercaya di Industri ITSK pada November 2023. Pedoman ini bertujuan untuk memitigasi risiko dan mendorong adopsi AI yang beretika di sektor ITSK. Dengan mendorong inovasi yang bertanggung jawab dan melindungi kepentingan konsumen, strategi ini berupaya untuk membuka potensi penuh dari data alternatif dalam penilaian kredit.
meningkatkan akuisisi konsumen. Inovasi di sektor keuangan bergerak ke arah model bisnis yang memanfaatkan Artificial Intelligence (AI) untuk mempercepat pemetaan kebutuhan konsumen dan pemanfaatan blockchain dalam mempercepat transaksi yang sifatnya granular ( atomic transaction). Dalam mengakomodir laju percepatan inovasi di sektor keuangan, OJK terus berupaya untuk menyeimbangkan antara velocity dan quality.
2.2.1 Innovative Credit Scoring (ICS)
2.2.1 Innovative Credit Scoring (ICS)
The application of AI in the financial sector can be observed in the Innovative Credit Scoring business model. Unlike traditional credit assessment models that heavily rely on individual credit history, ICS leverages artificial intelligence (AI), machine learning, and alternative data sources (such as utility and mobile phone payments, social media activity, online behavior, and e-commerce transaction history) to facilitate broader credit access for consumers.
Penggunaan AI dalam sektor keuangan dapat ditemukan dalam model bisnis Innovative Credit Scoring. Berbeda dengan model penilaian kredit tradisional yang sangat bergantung pada riwayat kredit individu, ICS memanfaatkan kecerdasan buatan (AI), machine learning, dan sumber data alternatif—seperti pembayaran tagihan listrik dan telepon seluler, aktivitas di media sosial, perilaku online, serta riwayat transaksi e-commerce—untuk memperluas akses kredit bagi konsumen
Gambar 9 : Klasifikasi Data Alternatif Sumber
: Hong Kong Monetary Authority (HKMA) (2020)
Figure 9 Source
: Classification of Alternative Data
: Hong Kong Monetary Authority (HKMA) (2020)
• Bank cashflow activity profiles • POS payment transaction profiles • Supply-chain payment data • Utility transaction profiles: electricity consumption • Telco transaction profiles • Shipping records and logistics data • Account records • ERP database: Invoice records, A/R records • Target customer profile • Quality of customers • Quality of transactions • Risk of fraudulence • Company's credits analysis reports • Personal credit reports • Data from business lending partnerships (Google, Alibaba and Sam's Club) • 3rd party business/products/services review (e.g. Alexa Global Rank, Yelp, Foursquare, Amazon, and eBay) • Risk characteristics • Psychometric test • Sentimental analysis • Intellectual properties: patents, trademarks, etc. • Physical asset value • Industry recognitions: awards • Size of customer base
Cashflow data
Transactional data
Non-Cashflow data
Alternative data
External Reports
Non- transactional data
Behavioural traits
2.2.2 Agregator
2.2.2 Agregator
Other reference data
Agregator merupakan kelompok terbesar ITSK yang memungkinkan konsumen untuk
Aggregators are the largest group within FSTI, enabling consumers to compare financial
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