Etiqueta: bip

IA Generativa e Otimização de Processos

A inteligência artificial generativa se encontra com a tecnologia para impulsionar a inovação no cenário corporativo, onde a diferenciação é peça-chave. Nesta era digital em constante mudança, empresas buscam diferenciais competitivos que as elevem acima da multidão. A IA generativa surge como uma ferramenta poderosa para desbravar novas fronteiras e transformar como os indivíduos operam.

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Contextos da Resolução BCB n.º 265/2022

Neste artigo, apresentamos os contextos da Resolução BCB n.º 265/2022, analisando os principais impactos e fornecendo orientações para a adaptação dos conglomerados tipo 3 às novas exigências.

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Resolução BCB n.º 229/22: contexto, principais mudanças e impactos

Desde a crise de 2008, o Basel Committee on Banking Supervision (BCBS), implementou uma série de medidas com o objetivo de fortalecer a supervisão, regulação e gestão de riscos dos bancos em todos os países signatários. Este conjunto de medidas foi denominado “Basileia III” e sua última fase de implementação pelo Bank for International Settlements (BIS) foi publicado em dezembro de 2017 por meio do documento “Basel III: Finalising post-crisis reforms”.

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Impact and changes in business and tech investment in FSI from the banking crisis

The US economy got off to a rough start in Q1 from the increases in the benchmark federal funds rate by the Federal Reserve to curb persistent inflation. The approval of another quarter-point interest rate in the May FOMC meeting marked the central bank’s tenth consecutive rate hike in 14 months. The fastest rate-raising cycle in 40 years, unhedged interest rate and market risks, and fear of contagion contributed to the recent collapses of Silicon Valley Bank, Signature Bank, and First Republic Bank. The failure of Silicon Valley Bank had significant ramifications for the tech sector due to the concentration of depositors within the technology and venture capital. More broadly, the banking crisis is having a slow-burn impact on the economy that will reduce credit flow to Main Street, delay investments, and put a brake on the technology investment boom from covid pandemic. The impact is reverberating through the financial system and bank’s operations in the US and beyond.   Despite the banking crisis, the Financial Services Industrial (FSI) has seen many Fin Tech innovations introduced in recent years, including blockchain-based cryptocurrencies, feature-rich mobile banking applications, and JP Morgan’s launch in the metaverse in Decentral, successful consumer adoption of Bank of America’s AI-powered chatbot, and increased use of cloud services such as AWS, Azure and GCP for enterprise computing.  In addition to the technology investments, banks have undertaken a large portfolio of Change the Bank (CTB) and Run the Bank (RTB) initiatives to improve operational efficiency, reduce costs, stay competitive, and remain compliant with the regulations.   Q1 earnings for big banks such as JPMorgan Chase, Bank of America, Citigroup, and Wells Fargo have benefited from the same heightened interest rates that tipped regional banks. However, the strains on the US banks will continue as the interest rate hikes work through the system and market participants, creating headwinds for banks due to a lag in monetary policy impact. This has prompted banks to re-assess their business operations, re-size business portfolios, and optimize technology investments.    In the last decade, banks have made sizeable investments in innovative changes and digital transformations with a time horizon of 12 to 24 months or more to bring in results. With technology costs going up, there is not an unlimited amount that banks can spend. Therefore, a shortened timeline for return on investment (ROI) is prioritized before the budget is allocated from the annual planning process. Banks are also trimming and streamlining their information technology spending amid worries about a possible recession later this year. Reining in cloud costs, removing redundant applications, and reducing vendor spend are among the challenging tasks for bank CIOs while ensuring the core banking operation is secure and resilient.   Meanwhile federal regulators are considering stricter rules for midsize banks due to recent failures. Tougher capital and liquidity and “stress tests” requirements are being discussed. These rules will target banks with between $100 billion to $250 billion in assets, which at present escape the most onerous capital requirements. The new rules will reverse the 2018 shift by lowering the $250 billion threshold in bank assets for supervision. These added regulatory and compliance requirements will increase the cost of regulatory compliance. As a result, banks may need to continue to prioritize investments and enhance capabilities in risk management and regulatory compliance.   With JPMorgan’s takeover of First Republic Bank early May, the trend towards consolidation in the U.S. banking sector is likely to continue. It’s widely expected that capital will become less available as lending standards are tightened. Companies may have to pay up to borrow or struggle to fund new projects. The market dislocation and disruption have served as inflection points for some banks. The same can be said about the technology-induced disruptions and their impact on some banks who will come out as winners or losers, depending on the decisions they make.   As US banks continue to face challenges from the recent banking crisis to the impact of interest rate hikes, stricter compliance requirements, and digital transformation. While some banks are struggling to keep up, others are leveraging technology and innovation to stay ahead of the competition. The challenge and success often lie in bank’s ability to adapt to the new normal and achieve a dedicated balance to keep the lights on, grow its business, mitigate regulatory and operational risks, while investing in innovation and technology in these uncertain times. About BIP.Monticello   BIP.Monticello, a member of the BIP Group, is a management consulting firm supporting the financial services industry with its expertise in digital transformation, change management, and financial services advisory. Our understanding of the competitive forces reshaping business models in capital markets and digital banking are proven enablers that help our clients drive innovative change programs to be more competitive and gain market share in new and existing businesses. In partnership with Bip.xTech, we collaborate with our clients to infuse the spirit of data-driven organizations and build digital solutions, helping them make their operations more efficient and achieve a competitive advantage in the marketspace.

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AI/ML: A Glimpse into the Future of Testing

“Artificial Intelligence is more profound than fire, electricity, or the internet” -Sundar Pichai, Chief Executive Officer of Alphabet Background on AI/ML and Software Testing Trends Artificial Intelligence (AI) and Machine Learning (ML) have gained significant attention due to their disruptive potential across industries. In the context of software testing, AI/ML can enhance testing methodologies and processes to achieve more effective and efficient results. AI continues to enter domains previously reserved for human skills and the results have been staggering. Machine Learning (ML), a subset of AI, further enables systems to learn and improve continuously through the application of algorithms. What is AI/ML-based testing? AI/ML-based testing methodologies have the potential to revolutionize test case creation, maintenance, and analysis, leading to expanded test coverage, improved accuracy, cost savings, and faster times to market. Such algorithms can access test data, make sense of it through pattern identification, and then utilize these predictions to successfully streamline software testing – thus simplifying test creation, reducing test maintenance, and driving new ways to assess the results. As we delve into the future of AI/ML in software testing, it becomes clear that testing teams need to prepare themselves for the upcoming advancements in this field. Test Case Creation: Using the analogy of self-driving cars, AI/ML tools are more similar to driving assistance than an actual driverless car1. In other words, AI technologies allow the user to write test cases manually while a machine automates them. AI/ML technologies augment test case creation by identifying reusable components and automating test authoring. ML models can further improve the test suite by identifying areas for enhancement and generating additional test cases. Test Case Analysis: AI/ML-based validation tools can automatically analyze code, identify bugs, and detect even the most granular changes. This thorough analysis allows testers to focus on high-risk areas and improve code quality. Test Case Maintenance: AI/ML can help answer the question, “If I’ve made a change in this piece of code, what’s the minimum number of tests I should be able to run to figure out whether this change is good or bad? 2” AI/ML tools employ self-learning capabilities to detect updates, upgrades, and code changes that require modifications in test scripts. These tools automatically revise test scripts, reducing the need for manual intervention. Additionally, ML models can identify the minimum number of tests needed to verify changed code, thus minimizing redundant test execution. Benefits of AI/ML in Testing By leveraging AI/ML in the testing process, organizations can unlock several benefits3. This section discusses the advantages of AI/ML-based testing: More Expansive Test Coverage: Applying automation and AI/ML to testing can increase the overall depth and scope of tests. As expected, automation allows for a significantly higher degree of test execution. AI/ML tools can learn from user sessions, identify missing tests, and self-correct, resulting in improved test coverage. Improved Accuracy / Defect Reduction: Automation reduces errors in software testing, while AI/ML algorithms continuously update themselves to enhance accuracy. AI/ML-based testing aids in defect identification and offers opportunities for precise defect prediction. Cost Savings and Faster Time to Market: Manual software testing is time-consuming and expensive. AI/ML-driven testing accelerates the process, reduces redundancy, and improves productivity. Faster test execution and the ability to rerun tests at a rapid pace leads to cost savings and a faster time to market. Looking Ahead The future of AI/ML in software testing holds great promise. Ongoing research in ML automation coupled with increased adoption of AI/ML testing applications is expected to drive significant improvements. Testing teams must prepare for these advancements by staying updated with evolving trends and acquiring the necessary skills and tools. Conclusion AI/ML-based testing is poised to reshape the software testing landscape, offering enhanced test case creation, maintenance, and analysis capabilities. The benefits include expansive test coverage, improved accuracy, cost savings, and faster times to market. As AI/ML continues to evolve, testing teams must embrace these trends and equip themselves with the knowledge and tools necessary for its overall adoption and successful implementation. About BIP.Monticello BIP.Monticello, a member of the BIP Group, is a management consulting firm supporting the financial services industry with its expertise in digital transformation, change management, and financial services advisory. Our understanding of the competitive forces reshaping business models in capital markets and digital banking are proven enablers that help our clients drive innovative change programs to be more competitive and gain market share in new and existing businesses. Sources 1.      AI In Test Automation: Here’s How It Works 2.      5-Great Ways to Use AI in Your Test Automation 3.      AI in Software Testing – Benefits, Approaches, Tools to Look in 2022 (testingxperts.com)

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