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    Breaking Banks

    Episode 501: A Financially Safer World & Fintech4Good

    Thu Jul 20 2023
    GuatemalaMoney Laundering PreventionFraud DetectionArtificial IntelligenceBlue CarbonNature-Based SolutionsValuationFinancingBlockchain

    Description

    The episode covers topics ranging from Guatemala's rich Mayan history to the challenges of money laundering prevention globally. It explores the role of artificial intelligence in fraud detection, the impact of money laundering on major cities, and the importance of blue carbon and nature-based solutions. The valuation and financing of nature-based projects, as well as the potential of blockchain in blue carbon management, are also discussed.

    Insights

    Money laundering prevention is ineffective globally

    Efforts to prevent money laundering have low success rates, with only 1% to 2% of cases being detected.

    Fraud prevention requires a multi-layer approach

    To effectively combat fraud, a combination of device recognition, behavioral biometrics, account-centric fraud detection, and omni-channel approaches should be implemented.

    Collaboration and data sharing are crucial in AML efforts

    Limited visibility, lack of collaboration, and political barriers hinder anti-money laundering efforts. Collaboration and international data sharing agreements are necessary to address these challenges.

    Blue carbon plays a significant role in carbon storage

    Coastal ecosystems like tidal marshes, mangroves, and sea grasses can capture and store carbon at a rate up to six times higher than mature tropical forests.

    Valuing nature-based solutions is complex

    Determining the value of different nature-based projects in the market is challenging due to various methodologies and factors like rainfall, biodiversity, soil health, and soil quality.

    Innovation and financing are key for nature-based solutions

    Finding the overlap between the financial system and nature-based solutions, exploring new financing options like NFTs, and leveraging AI and remote monitoring can drive innovation and support for environmental impact projects.

    Blockchain technology can enhance blue carbon management

    Blockchain offers solutions for managing and measuring blue carbon, ensuring data immutability, preventing double spending, and enabling transparent transactions in the space.

    Chapters

    1. Guatemala and Moneta Plus
    2. Challenges in Money Laundering Prevention
    3. Fraud Prevention and Machine Learning
    4. Money Laundering and Global Impact
    5. Blue Carbon and Nature-Based Solutions
    6. Valuing Nature-Based Solutions
    7. Innovation and Financing for Nature-Based Solutions
    8. Blockchain and Blue Carbon Management
    Summary
    Transcript

    Guatemala and Moneta Plus

    00:06 - 08:52

    • Guatemala is the first country in Central America with a rich Mayan history.
    • Antigua, Guatemala was founded by the Spanish in the 1500s or 1600s and is surrounded by three volcanoes.
    • Moneta Plus, a fintech organization, is based in Guatemala due to its CEO's decision after IBM sold its operations in Central America.
    • Moneta Plus started as a software company and later specialized in fraud prevention and money laundering detection.
    • They developed a real-time engine called the X project, which was an early form of artificial intelligence.
    • Moneta Plus incorporated neural networks into their models for fraud management and money laundering detection.
    • Money laundering globally remains difficult to prevent, with only about 1% to 2% of cases being detected.

    Challenges in Money Laundering Prevention

    08:29 - 16:40

    • Money laundering prevention is ineffective globally, with only 1% to 2% of money laundering being stopped.
    • Regulations focus on compliance rather than addressing the real problem.
    • The industry uses a game of whack-a-mole to identify suspicious transactions, resulting in many false positives.
    • The future of money laundering prevention should be approached like cybersecurity, tracking and stopping suspicious behavior.
    • Fraud monitoring allows for feedback and verification, while AML lacks this feedback loop.
    • Chinese mobile wallets have significantly lower fraud rates compared to credit cards due to modern tech stacks and biometrics-based identity systems.
    • Fixing identity and core technology is crucial for fraud prevention.
    • Internal fraud within organizations is a significant issue, especially in developing countries.
    • A multi-layer approach to fraud prevention is recommended, including device recognition, behavioral biometrics, account-centric fraud detection, and omni-channel approaches.
    • Advancements in rule-based fraud management have been effective due to expert judgment.

    Fraud Prevention and Machine Learning

    16:13 - 24:27

    • Rule-based fraud prevention is still valuable and relies on expert judgment.
    • Machine learning requires a lot of data and training to be effective in fraud prevention.
    • An ensemble of rule-based systems and machine learning is a powerful combination for accurate fraud detection.
    • Accessing the right data from disparate legacy systems is a major challenge for implementing fraud prevention in banks.
    • Orchestration of different systems is the future of fraud prevention to ensure they communicate and share information.
    • Automation can help address the challenges faced by fraud units in terms of time constraints and prioritization.
    • The future of fraud prevention lies in machine learning, data availability, and orchestration.
    • AML (Anti-Money Laundering) efforts are ineffective due to limited visibility, lack of collaboration, and political barriers to cross-border data sharing.
    • Collaboration and international data sharing agreements are necessary to effectively combat money laundering.

    Money Laundering and Global Impact

    24:02 - 32:12

    • New York, London, and Dubai are major cities that make a lot of money out of money laundering.
    • Charlie Shrem, founder of Bit Instant, went to prison for two years for money laundering.
    • HSBC had a $1.7 billion fine for money laundering in Mexico but no one from the bank went to jail.
    • The banks make a lot of money from money laundering, which is why it continues to be a problem.
    • There are efforts from regulators to collaborate and share information on anti-money laundering.
    • Artificial intelligence is having an impact on the monitoring and prevention of money laundering.
    • AI systems provide a hybrid approach between expert models and AI-based continuous learning detection models.
    • There is still a need for human input and balance between human and machine in AI systems.
    • A conference in Guatemala discussed artificial intelligence, money laundering, fraud prevention, and banking innovation.

    Blue Carbon and Nature-Based Solutions

    31:50 - 38:40

    • Blue carbon is important because 80 to 90 percent of the world's carbon is stored in the ocean.
    • Whales capture a ton of carbon when they die and sink to the bottom of the ocean, so protecting whales can sequester more carbon in the oceans.
    • Blue carbon projects are less common than land-based ones because measuring, reporting, and verifying blue carbon is more challenging.
    • Coastal ecosystems like tidal marshes, mangroves, and sea grasses can capture and store carbon at a rate up to six times higher than mature tropical forests.
    • Financial dynamics come into play when it comes to blue carbon projects as customers need to know how much they can claim and retire for their net zero commitments or compliance with carbon markets.
    • The rights to claim blue carbon are complex due to commons-based governance of the oceans compared to private or public property rights on land.
    • Private ownership of all the oceans has been suggested as a solution, but accountability for environmental costs would be crucial in such a system.

    Valuing Nature-Based Solutions

    38:24 - 45:05

    • Comparing green carbon credits and blue carbon credits in terms of exchange
    • Carbon markets struggle to obtain ongoing quality data about the efficacy of carbon credits
    • Different methodologies like biochar, direct air capture, and enhanced rock weathering have different yield curves
    • It is challenging to determine the value of different nature-based projects in the market
    • Artificial intelligence can help with valuing and understanding the distribution of these projects
    • Causal AI and the Shapley value concept can be applied to forests to determine contributions and payoffs
    • Including variables like rainfall, biodiversity, soil health, and soil quality adds complexity to valuing nature-based solutions

    Innovation and Financing for Nature-Based Solutions

    44:37 - 51:33

    • Rainfall, biodiversity, soil health, and soil quality are variables in the game.
    • Understanding nature in a reductionist way is not necessary for innovation.
    • The focus should be on finding the overlap between the financial system and nature-based solutions.
    • Financing projects like Blue Carbon can be expensive but NFTs could bring liquidity to these projects.
    • Using AI and remote monitoring can provide data for evaluating project success.
    • Creating a forward market for environmental impact projects would drive innovation and support.
    • IoT and drones can be used for measurement and monitoring of natural resources.

    Blockchain and Blue Carbon Management

    51:04 - 57:01

    • Mangroves and green carbon are measurable, but pollution from algae growth can be a problem.
    • Financial institutions are using blockchain to prevent double spending and ensure data immutability.
    • Adoption of blockchain technology is the solution for managing and measuring blue carbon.
    • There will be new standards announced by the task force on nature-related financial disclosures in September.
    • It's important to measure and find blue carbon accurately and avoid double spending.
    • Blockchain has clear use cases in this space.
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