IBOV 166,334.86 ▼ 0.27% IPSA 11,186.57 ▲ 0.34% IPC MEX 64,301.04 ▲ 0.07% MERVAL 2,891,651 ▼ 1.89% COLCAP 2,461.23 ▲ 0.36% BVL PERÚ 58,401.58 ▼ 1.35% USD/BRL5.22▲ 0.02% USD/MXN17.04▼ 0.16% USD/CLP927.14▲ 1.17% USD/COP3,092▼ 1.30% USD/PEN3.37▼ 0.07% USD/ARS1,495▲ 0.45% USD/UYU40.26▲ 1.93% USD/PYG6,002▲ 2.02% USD/BOB11.48▲ 0.10% USD/DOP58.50▲ 1.15% USD/CRC444.65▲ 1.69% USD/GTQ7.62▲ 2.33% USD/HNL26.80▲ 1.74% USD/NIO36.62▲ 0.69% USD/VES773.40▲ 0.14% USD/PAB1.00— 0.00% USD/BZD2.00— 0.00% USD/JMD 157.28 — 0.00% USD/TTD6.71▲ 1.08% EUR/BRL6.05▲ 0.56% BRENT 88.88 ▼ 0.03% WTI 83.11 ▼ 0.11% IRON ORE 161.91 — — COPPER 6.61 ▲ 0.03% GOLD 4,461 ▲ 1.78% SILVER 65.59 ▲ 1.26% SOY 1,184 ▲ 3.20% CORN 480.50 ▲ 10.02% WHEAT 655.00 ▲ 3.93% COFFEE 317.25 ▼ 5.51% SUGAR 16.43 ▼ 1.79% ORANGE JUICE 138.55 ▼ 0.47% COTTON 85.03 ▲ 2.33% COCOA 5,719 ▲ 3.18% BEEF 223.60 ▼ 3.93% CATTLE 339.10 ▼ 3.16% LITHIUM 75.20 ▲ 1.47% PETR4 41.64 ▼ 0.05% VALE3 72.97 ▲ 0.83% ITUB4 38.60 ▼ 1.03% BBDC4 16.85 ▲ 0.36% ABEV3 14.89 ▼ 0.80% BBAS3 19.37 ▲ 0.47% B3SA3 14.26 ▼ 0.21% WEGE3 47.59 ▲ 0.49% PRIO3 59.14 ▼ 0.19% SUZB3 41.33 ▲ 2.35% RENT3 34.68 ▼ 0.09% AZZA3 15.89 ▼ 2.63% CSAN3 3.22 ▼ 1.83% RAIZ4 0.25 — 0.00% PCAR3 2.75 ▼ 0.36% GMAT3 3.65 ▼ 1.08% PSSA3 48.13 ▼ 0.54% CVCB3 1.33 ▼ 2.92% POSI3 3.36 ▲ 2.44% SLCE3 13.34 ▲ 0.30% NATU3 8.14 ▼ 0.73% IBOV 166,334.86 ▼ 0.27% IPSA 11,186.57 ▲ 0.34% IPC MEX 64,301.04 ▲ 0.07% MERVAL 2,891,651 ▼ 1.89% COLCAP 2,461.23 ▲ 0.36% BVL PERÚ 58,401.58 ▼ 1.35% USD/BRL 5.16 ▲ 0.01% USD/MXN 17.06 ▼ 0.24% USD/CLP 913.98 ▲ 0.04% USD/COP 3,140 ▲ 0.03% USD/PEN 3.36 ▼ 0.66% USD/ARS 1,493 ▲ 0.10% USD/UYU 40.27 ▲ 1.24% USD/PYG 5,939 ▲ 1.68% USD/BOB 11.64 ▼ 0.76% USD/DOP 58.34 ▲ 1.25% USD/CRC 445.92 ▲ 0.89% USD/GTQ 7.62 ▲ 2.21% USD/HNL 26.79 ▲ 1.57% USD/NIO 36.62 ▲ 0.69% USD/VES 762.44 ▼ 0.13% USD/PAB 1.00 — 0.00% USD/BZD 2.00 — 0.00% USD/JMD 157.28 — 0.00% USD/TTD 6.70 ▲ 0.61% EUR/BRL 5.95 ▲ 1.01% BRENT 88.88 ▼ 0.03% WTI 83.11 ▼ 0.11% IRON ORE 161.91 — — COPPER 6.61 ▲ 0.03% GOLD 4,461 ▲ 1.78% SILVER 65.59 ▲ 1.26% SOY 1,184 ▲ 3.20% CORN 480.50 ▲ 10.02% WHEAT 655.00 ▲ 3.93% COFFEE 317.25 ▼ 5.51% SUGAR 16.43 ▼ 1.79% ORANGE JUICE 138.55 ▼ 0.47% COTTON 85.03 ▲ 2.33% COCOA 5,719 ▲ 3.18% BEEF 223.60 ▼ 3.93% CATTLE 339.10 ▼ 3.16% LITHIUM 75.20 ▲ 1.47% PETR4 41.64 ▼ 0.05% VALE3 72.97 ▲ 0.83% ITUB4 38.60 ▼ 1.03% BBDC4 16.85 ▲ 0.36% ABEV3 14.89 ▼ 0.80% BBAS3 19.37 ▲ 0.47% B3SA3 14.26 ▼ 0.21% WEGE3 47.59 ▲ 0.49% PRIO3 59.14 ▼ 0.19% SUZB3 41.33 ▲ 2.35% RENT3 34.68 ▼ 0.09% AZZA3 15.89 ▼ 2.63% CSAN3 3.22 ▼ 1.83% RAIZ4 0.25 — 0.00% PCAR3 2.75 ▼ 0.36% GMAT3 3.65 ▼ 1.08% PSSA3 48.13 ▼ 0.54% CVCB3 1.33 ▼ 2.92% POSI3 3.36 ▲ 2.44% SLCE3 13.34 ▲ 0.30% NATU3 8.14 ▼ 0.73%
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Wednesday, August 19, 2026

Brazil Business - Brazil

Brazil: Big data and automation help credit industry democratize access

By · January 25, 2022 · 2 min read

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RIO DE JANEIRO, BRAZIL – The financial system and the credit industry have been undergoing constant transformations, notably with respect to the analysis of credit concessions to borrowers.

With so many innovations in platforms such as Pix, Open Banking and Cadastro Positivo giving access to new customers and markets, it is expected that the data generated by these systems will enable the creation of new services and products, for instance.

Granting credit is no longer an investigation of the past, but an assessment of the future. (photo internet reproduction)
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In this context, granting credit transcends the analogical method of data verification, based on social capital: the main problem to be solved to reach a diagnosis is no longer investigating the past, but instead assessing the future of the borrower’s activity, through technologies based on big data and artificial intelligence (AI).

In the past, the information made available by government systems and agencies responsible for consumer borrower data has in fact always been very accurate. However, lacking consolidation, the time to collect, process, and even make this data available was tremendous.

Big data came to organize and consolidate the information in single platforms, allowing the lender to perform this same assessment in seconds, with only one CPF (Natural Persons Register) or CNPJ (National Registry of Legal Entities), for instance.

This modernization in the credit industry has created new opportunities for lenders, and has become a competitive agent. In other words, the faster and better the response, the better the credit. Nowadays it is possible to respond to limit analysis, concession and even make a PLD (Money Laundering Policy) with the support of big data systems.

It is possible to schedule queries in an extremely fast way, to mitigate analysis risks and even to program alerts for potential systemic issues or information inconsistency. These new techniques and the creation of methodology for research and data collection are contributing factors when it comes to the evolution of the credit industry.

All of this refinement and gains resulting from this system are a significant addition to the value of institutions’ businesses.

This article was produced by The Rio Times’ automated newsroom system. How we use AI · Report an error

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