Digital Finance: Blockchain, AI, Big Data, Cybersecurity & FinTech
Blockchain and distributed ledger technology
Blockchain is a technology created in 2008 by Satoshi Nakamoto together with Bitcoin. It is a distributed digital ledger that records transactions in blocks linked together using cryptography. The main purpose of blockchain is to allow secure transactions without a central authority, creating trust through technology.
Blockchain is based on cryptography, which protects information using hash algorithms and digital signatures. Cryptography ensures confidentiality, integrity, authentication, and non-repudiation. These principles guarantee that data is secure, cannot be altered, and that users cannot deny their transactions.
Each block contains transaction data, the hash of the previous block, its own hash, and a digital signature. Blockchain mainly uses the SHA-256 hash algorithm. A very important feature is the avalanche effect, where a small change in data creates a completely different hash. This makes blockchain immutable, because changing one block would require changing all the following blocks, which is practically impossible.
Blockchain works as an immutable general ledger. All transactions are permanently recorded and shared across the network. This provides high levels of security, transparency, and trust, and allows both tangible and intangible assets to be registered safely. Blockchain operates in a decentralized peer-to-peer (P2P) network. There is no central authority and all nodes are equal. Each node stores a copy of the blockchain. If one node is attacked, the network detects the error and restores the correct information through consensus.
To add new blocks, blockchain uses mining. Mining validates transactions, creates new blocks, and introduces new cryptocurrencies. Miners compete to find a valid nonce that produces a hash below a target. This process is called Proof of Work (PoW) and requires high computational power, which prevents fraud.
Blockchain solves the Byzantine Generals Problem through consensus protocols, which allow the network to agree even if some participants are malicious. The main protocols are Proof of Work (PoW) and Proof of Stake (PoS). PoW is very secure but consumes a lot of energy, while PoS is more efficient and selects validators based on their stake.
There are different types of blockchains. Public blockchains are open and transparent but slower. Private blockchains are controlled by one entity and are faster but centralized. Hybrid blockchains combine features of both, offering a balanced solution. In conclusion, blockchain is a secure and transparent technology that combines cryptography, decentralization, consensus mechanisms, and immutable records. Its applications go beyond cryptocurrencies and include areas such as voting systems, degree verification, and supply chains, making it very important in modern finance.
Artificial intelligence in financial technology
Artificial Intelligence (AI) is a key technological development in Financial Technology. AI refers to systems that are able to simulate human intelligence, such as learning, reasoning, and decision-making. Today, AI is widely used in business and finance thanks to the availability of big data, which is the main fuel for AI models. The origins of AI are not recent. In 1950, Alan Turing introduced the idea of machine intelligence and proposed the Turing Test, which evaluates whether a machine can behave like a human. Later, in 1956, the term Artificial Intelligence was officially created.
Since then, several milestones have shown the evolution of AI, such as IBM Deep Blue beating the world chess champion in 1997, IBM Watson understanding natural language in 2011, and AlphaGo defeating the world Go champion in 2016. These events proved that AI can outperform humans in specific tasks.
There are two main types of AI: discriminative (predictive) AI and generative AI. Discriminative AI is used to classify and predict outcomes based on existing data, such as fraud detection, demand forecasting, or credit risk analysis. Generative AI, on the other hand, is capable of creating new content, such as text, images, music, or code, based on patterns learned from data. Examples include chatbots and image generators. A key limitation of generative AI is the hallucination effect, where the system may generate incorrect or invented information.
AI systems are mainly based on neural networks and deep learning. Neural networks are inspired by the human brain and are made up of artificial neurons organized in layers: input layer, hidden layers, and output layer. These models learn by adjusting weights through a training process that minimizes errors. Deep learning allows AI to handle complex problems and large amounts of data efficiently.
A very important development in AI is Large Language Models (LLMs). LLMs are neural networks trained with massive amounts of text data and are capable of understanding, generating, and summarizing language. They are used in translation, customer service chatbots, content creation, and programming support. However, they are expensive to train and may produce biased or incorrect outputs.
AI can be applied to business in three main ways: process optimization, cost reduction, and profit maximization. For example, AI can optimize warehouse logistics, reduce energy consumption in data centers, or improve marketing strategies using online learning techniques. AI also plays a central role in Industry 4.0, where automation, robotics, and intelligent systems transform supply chains, services, and employment models. In conclusion, Artificial Intelligence is a fundamental technology in modern finance and business. Through data, algorithms, and learning models, AI improves efficiency, decision-making, and innovation. Despite its limitations and ethical challenges, AI will continue to play a crucial role in the digital economy.
Big Data: definitions and data management
Big Data is a disruptive technology that refers to the management and analysis of very large volumes of data that cannot be processed with traditional systems. In finance, Big Data is important because companies and banks generate huge amounts of data from transactions, customers, and digital activity. The goal of Big Data is to transform data into useful information for decision-making.
Big Data is defined by the 5 V’s:
- Volume — the large amount of data
- Velocity — the speed at which data is generated and processed
- Variety — the different formats of data, especially unstructured data
- Veracity — the reliability and quality of data
- Value — data is useful only if it creates business benefits
A key element of Big Data is the Data Lake, which is a centralized storage system that stores data in its raw format. It can store structured, semi-structured, and unstructured data. This is different from a data warehouse, which only stores processed and structured data for reporting.
There are three main data types:
- Structured data — organized in tables and databases
- Semi-structured data — uses tags or labels, such as JSON or XML
- Unstructured data — has no fixed format, such as emails, videos, or social media posts
Most Big Data today is unstructured, which makes it more complex to analyze. Data obtaining consists of collecting data from internal sources (company systems, transactions) and external sources (websites, sensors, social networks). The data is then stored in the Data Lake so it can be analyzed. Without data obtaining, Big Data projects cannot work.
In Big Data, non-relational databases are mainly used. Relational databases store data in tables and are suitable for structured data, but they do not scale well with large volume, velocity, and variety. Non-relational databases are more flexible and better adapted to Big Data environments.
Data processing transforms raw data into information. It includes data cleaning, data exploration, and Machine Learning techniques. Supervised learning uses labeled data for prediction and classification. Unsupervised learning works with unlabeled data to find patterns and group data. Data processing allows companies to extract knowledge from data. In conclusion, Big Data is essential in finance because it allows companies to manage different data types, store them efficiently, process them correctly, and extract value for better decisions.
Cybersecurity principles and risk management
Cybersecurity is the ability of an organization to reduce the risk that information faces from digital threats. Its main objective is the protection of digital information and information systems. Information is one of the most valuable assets for companies and financial institutions, and it must be protected from attacks, errors, and misuse.
Cybersecurity is based on the CIA Triad, which defines the three main security objectives:
- Confidentiality — information is only accessible to authorized users
- Integrity — data is accurate and has not been altered
- Availability — authorized users can access information when needed
To achieve these objectives, organizations apply layered security, meaning that no single control is enough to protect information. A key concept in cybersecurity is the difference between vulnerabilities and threats. A vulnerability is a weakness in a system that can be exploited. A threat is any action that takes advantage of a vulnerability to cause damage. Threats can be internal or external and may come from malicious attacks, human errors, or poor system design.
The most common cybersecurity threats include social engineering, malware, and denial of service attacks. Social engineering, especially phishing, is the main cyber risk in the financial sector and consists of manipulating users to obtain confidential information. Malware is malicious software designed to damage systems or steal data, including ransomware. Denial of Service (DDoS) attacks aim to make systems unavailable by overwhelming them with traffic.
Cybersecurity risk management focuses on mitigation techniques to reduce risk to an acceptable level. These include access control, encryption, monitoring and logs, secure configurations, and user awareness and training. Cybersecurity is a continuous process and must involve all levels of the organization, especially senior management.
Organizations must also be prepared to respond to incidents and recover from attacks. This is known as cyber-resilience, which refers to the ability to resist, recover, and adapt after a cyber incident. Techniques such as pentesting (security testing) and compliance with international standards help organizations improve their security level. In conclusion, cybersecurity is essential in finance to protect information, ensure trust, and maintain business continuity. By managing risks, understanding threats, and applying mitigation measures, organizations can reduce their exposure to cyberattacks.
FinTech verticals and platform types
FinTech (Financial Technology) refers to the use of technology and innovation to provide financial products and services. FinTech companies are classified into different types or verticals depending on the service they offer, which together form the FinTech ecosystem.
One main vertical is Advice and Wealth Management (WealthTech), which includes roboadvisors that automatically manage investment portfolios using algorithms and AI, and social trading platforms, where users share or copy investment strategies. Another important vertical is Personal Finance Management, which helps users control expenses, monitor accounts, and compare financial products.
Alternative financing is a key FinTech type and includes quick online loans and crowdfunding platforms. Crowdfunding connects promoters with many investors and can be donation-based, reward-based, equity-based (crowdequity), or debt-based (crowdlending or P2P lending).
Payment services are one of the most developed FinTech areas, allowing digital and mobile payments that replace physical cash. FinTech also includes Big Data and analytics platforms, which use large volumes of data and AI to improve fraud detection, risk management, and personalization. Another subtype is Online customer identification, which uses biometric technologies such as facial or fingerprint recognition.
Cryptoassets and Blockchain-based FinTech form another category, where digital assets are supported by cryptography and distributed ledgers to ensure security and traceability. InsurTech applies FinTech solutions to the insurance sector, improving efficiency and customer experience, while PropTech applies technology to real estate activities such as buying, selling, managing, and financing property. Finally, Neobanks or Challenger Banks are fully digital banks with no physical branches that offer traditional banking services through online platforms with lower costs and better user experience.
Regarding financial platforms, they are digital infrastructures that act as intermediaries between users and financial services. The main types are FinTech platforms, InsurTech platforms, Crowdfunding platforms, Digital payment platforms, Peer-to-Peer lending platforms, and Roboadvisor platforms, each specialized in a specific financial function.
Cryptoassets, tokens, and decentralized finance
Cryptoassets are financial assets that exist in digital form and use cryptography to guarantee security. Their origin is closely linked to the development of blockchain technology and the creation of Bitcoin in 2009 by Satoshi Nakamoto.
Blockchain is a public, decentralized, immutable, and transparent ledger where transactions are recorded in blocks and validated by a distributed network, without the need for traditional intermediaries such as banks or governments. Bitcoin was created to solve the double-spending problem, which is the risk of using the same digital money more than once. In traditional financial systems, this problem is solved through centralized control. In cryptoassets, blockchain solves it by allowing all network participants to verify and record transactions, ensuring trust through technology rather than institutions.
After Bitcoin, other cryptocurrencies such as Ethereum, Litecoin, and Ripple appeared, expanding the crypto ecosystem and its possible uses. Cryptoassets can be classified into different types. Cryptocurrencies are mainly used as a medium of exchange and unit of account, similar to money. Tokens are digital units issued on blockchains and can represent assets, rights, or utilities. Tokens include utility tokens, which give access to services; security tokens, which represent ownership of real assets and are usually regulated; and governance tokens, which give voting rights in decentralized platforms.
Another important category is stablecoins, which are designed to reduce volatility by being linked to stable values. Stablecoins can be backed by fiat money, commodities, cryptocurrencies, or algorithms, and their main goal is to protect users in periods of high price instability. A further development in cryptoassets is the appearance of NFTs (Non-Fungible Tokens). NFTs are unique and non-interchangeable digital assets that represent ownership of digital or physical goods.
They are based on smart contracts and, although their market value has fallen, they remain an important technological innovation within the crypto ecosystem. Cryptoassets have also enabled the growth of Decentralized Finance (DeFi). DeFi refers to financial services built on blockchain that operate without traditional intermediaries. Through DeFi, users can access loans, exchanges, and other financial services in a more automated, transparent, and efficient way.
DeFi is closely related to tokenization, which is the process of creating a digital token that represents an asset, right, or utility on the blockchain. Tokenization increases efficiency, transparency, and accessibility in financial markets. A key element of this disruption is the use of smart contracts. Smart contracts are self-executing programs stored on the blockchain that automatically carry out agreements when predefined conditions are met. They reduce costs, eliminate intermediaries, and increase security. Smart contracts make it possible to create decentralized applications (DApps), which are applications that run on blockchain networks and are decentralized, transparent, and resistant to manipulation.
In conclusion, cryptoassets represent a major disruption of the traditional financial system. Through blockchain technology, new types of digital assets, tokenization, DeFi, smart contracts, and decentralized applications, cryptoassets are transforming how money, assets, and financial services are created and managed in the digital economy.