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A brief Overview on Finance and Technology for Solving Business problem
- 1. Computer Science in Business Problems WEEK 1 BY: MS SARAH AHMED Email ID: ssarahahmedd@gmail.com
- 2. OUTLINE What is Fintech What is Crowd Funding/Crowd Sourcing Analyzing FinTech’s Dimensions. Key Terms Subcategories of Fintech AI in FinTech When Not to Use AI in Fintech The Fintech Landscape In Pakistan Challenges Faced By Fintech Startups In Pakistan
- 3. OUTLINE KEY PLAYERS, PRODUCTS AND TRENDS Computer science and its applications to businesses
- 4. What is Fintech? The word ‘FinTech’ is simply a combination of the words ‘financial’ and ‘technology’. It describes the use of technology to deliver financial services and products to consumers. This could be in the areas of banking, insurance, investing – anything that relates to finance. Increasingly, FinTech is coming to represent technologies that are disrupting traditional financial services, including mobile payments, money transfers, crowdsourcing, loans, fundraising and asset management, Blockchain, and cryptocurrencies. What Is Crowdsourcing? Crowdsourcing involves obtaining work, information, or opinions from a large group of people who submit their data via the Internet, social media, and smartphone apps. People involved in crowdsourcing sometimes work as paid freelancers, while others perform small tasks voluntarily. For example, traffic apps like Waze encourage drivers to self-report accidents and other roadway incidents to provide real-time, updated information to app users.
- 5. Example of Crowdsourcing Companies that need some jobs done only on occasions, such as coding or graphic design, can crowdsource those tasks and avoid the expense of a full-time in-house employee. While crowdsourcing often involves breaking up a big job, businesses sometimes use crowdsourcing to assess how multiple people perform at the same job. For instance, if a company wants a new logo, it can have dozens of graphic designers assemble samples for a small fee. The company can then pick a favorite and pay for a more complete logo package.
- 6. What is Crowd Funding? Crowdfunding solicits money or resources to help support individuals, charities, or startups. People can contribute to crowdfunding requests with no expectation of repayment, or companies can offer shares of the business to contributors. Example of Crowd Funding: For example, popular crowdfunding platforms include Indiegogo and Kickstarter, both online platforms in which individuals can contribute a small amount of money and collectively bring a new business idea or product to fruition. Platforms like Kickstarter make money by charging a small platform fee, whereas some of the best crowdfunding platforms specialize in helping creatives (Patreon), investing (StartEngine), the real estate industry, nonprofits (Mightycause), or even startups trying to raise capital (SeedInvest Technology).
- 11. Key Terms B2C (Business to consumer) is a business model where products and services are sold directly to the consumer. B2B (Business to business) is a business that sells products or services to other businesses. B2G stands for Business-to-government, implying the relationship between a business to government agencies and government institutions. B2G is defined as a sales model when companies sell products, services and information to governments or government agencies (such as federal, state, or local agencies). Peer-to-peer lending is a way for people who need a loan to borrow money directly from investors, rather than from banks and other lenders. Traditional lending refers to arrangements where banks, credit unions and other financial institutions offer borrowers loans and supply the financing for these loans. Examples of traditional lending include applying for an auto loan at your local credit union or a mortgage from your bank.
- 12. Key Terms Cloud Computing: Cloud computing is the on-demand delivery of IT resources over the Internet with pay-as-you-go pricing. Instead of buying, owning, and maintaining physical data centers and servers, you can access technology services, such as computing power, storage, and databases, on an as-needed basis from a cloud provider like Amazon Web Services (AWS). Big Data: Big Data is a collective term that describes ever-growing, large, diverse, structured and unstructured, and hard-to-manage volumes of data gathered from social, machine, and transactional sources. Put simply; Big Data is a group of complex, unorganized data sets that grow exponentially in volume, variety, and velocity. • Big data as the three V’s: • Volume. Organizations collect data from a variety of sources, including transactions, smart (IoT) devices, industrial equipment, videos, images, audio, social media and more. In the past, storing all that data would have been too costly – but cheaper storage using data lakes, Hadoop and the cloud have eased the burden. • Velocity. With the growth in the Internet of Things, data streams into businesses at an unprecedented speed and must be handled in a timely manner. RFID tags, sensors and smart meters are driving the need to deal with these torrents of data in near-real time. • Variety. Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, emails, videos, audios, stock ticker data and financial transactions.
- 13. Key Terms Quantum computing is an area of computer science that uses the principles of quantum theory. Quantum theory explains the behavior of energy and material on the atomic and subatomic levels. Quantum computing uses subatomic particles, such as electrons or photons. Quantum bits, or qubits, allow these particles to exist in more than one state (i.e., 1 and 0) at the same time. ◦ Benefits of Quantum Computing: Financial institutions may be able to use quantum computing to design more effective and efficient investment portfolios for retail and institutional clients. They could focus on creating better trading simulators and improve fraud detection. The healthcare industry could use quantum computing to develop new drugs and genetically-targeted medical care. It could also power more advanced DNA research. For stronger online security, quantum computing can help design better data encryption and ways to use light signals to detect intruders in the system. Quantum computing can be used to design more efficient, safer aircraft and traffic planning systems.
- 14. Key Terms A blockchain is essentially a distributed database of records or public ledger of all transactions or digital events that have been executed and shared among participating parties. Each transaction in the public ledger is verified by consensus of a majority of the participants in the system. And, once entered, information can never be erased. The blockchain contains a certain and verifiable record of every single transaction ever made. Bitcoin, the decentralized peer to- peer digital currency, is the most popular example that uses blockchain technology.
- 15. Subcategories of Fintech FinTech is also frequently used as an umbrella term for various subcategories, such as WealthTech and RegTech Capital Markets Tech, in which companies leverage newer technology such as artificial intelligence, machine learning, and blockchain, is led by seasoned capital markets experts and is both collaborating with and disrupting the financial services holders. InsurTech is a combination of insurance and technology. It refers to innovations that generate efficiency and cost savings from the existing insurance industry model. RegTech is a community of technology companies that solve regulatory challenges through automation. The increase in major regulatory policy and the rise in digital products have made it imperative for companies to check for and implement compliance issues, and this can be difficult with old, manual processes.
- 16. Subcategories of Fintech PayTech refers to the combination of payments and technology. Innovative payment services now form part of the PayTech ecosystem and have dominated the early days of the FinTech revolution through mobile, cross-border, peer-to-peer, and cryptocurrency payments. Financial institutions have had to digitize their current offerings to create new channels linked to a digital platform. LegalTech combines the nature of legal technologies and their relationship with data, the Internet of Things (IOT), cybersecurity, and distributed ledger technologies as well as ethical considerations of the technological advancement. AI in Finance refers to how artificial intelligence, machine learning, and deep learning are applied across financial services companies today and how they could be used in the future. WealthTech unites wealth and technology to provide digital tools for personal and professional wealth management and investing. This sector includes brokerage platforms, automated/semi- automated robo advisors, and self-directed investment tools for individual investors and advisors to navigate the changing landscape in wealth management
- 17. Difference Between Fintech, Regtech and Suptech
- 19. AI in FinTech 90% of Fintech companies already use AI. AI has brought numerous benefits to FinTech, including personalized financial advice, faster fraud detection, increased productivity and improved accuracy. Fintech firms have seen an 80% improvement in speed and accuracy. It's no surprise that more than a third of all financial services companies have recently adopted AI.
- 20. AI in FinTech
- 21. AI in FinTech robotic process automation Robotic process automation (RPA) is a software technology that makes it easy to build, deploy, and manage software robots that emulate humans actions interacting with digital systems and software. Just like people, software robots can do things like understand what’s on a screen, complete the right keystrokes, navigate systems, identify and extract data, and perform a wide range of defined actions. Data automation/credit risk assessment credit scoring is an evaluation of how well a customer can pay and is willing to repay debts. AI decisions on credit scores are based on a lot of data, such as total income, credit history, transaction analyses, work experience, etc. Credit scoring is a mathematical model based on statistical methods that consider a large amount of information. The result is that credit scores using artificial intelligence provide sensitive, individual credit score assessments based on several additional real-time factors, potentially allowing more people with incomes to access finance.
- 22. AI in Fintech Detecting banking fraud Data is analyzed using AI-based analytics tools to detect suspicious transactions that may indicate fraud. Through artificial intelligence, users' behavior patterns can be monitored, and actions that deviate from the normal can be identified and could show fraud attempts or incidents. Enhanced Algorithmic Trading Algorithmic trading employs artificial intelligence (AI) technologies such as machine learning to conduct stock trades by following a pre-programmed set of instructions that analyze data to make trading decisions. AI has changed the typical trading desk by analyzing millions of data points in real-time and producing insights that statistical models cannot discover.
- 23. AI in Fintech Financial forecasts The use of artificial intelligence in finance means that it can identify, analyze and adjust changes in data patterns on its own. Moreover, even if data quality is far from perfect, artificial intelligence in fintech can still come up with valuable insights into the exchange rates, investments, short- or long-term trends predictions.
- 24. When Not to Use AI in Fintech Limited data availability. AI systems require substantial amounts of quality data to generate accurate and reliable predictions. If you're working with limited or insufficient data, the performance of AI algorithms can be compromised. In these situations, it's important to assess whether the data available is sufficient to support AI-driven solutions. Lack of domain expertise: AI applications require domain knowledge and expertise to develop and deploy effectively. If your team lacks the necessary understanding of AI technologies and their implications, implementing and maintaining AI-driven solutions may be challenging. In such cases, partnering with experts or investing in training and upskilling can help bridge the knowledge gap.
- 25. When Not to Use AI in Fintech Low complexity and repetitive tasks: If the task at hand is relatively simple, repetitive, and doesn't require complex decision-making or analysis, AI may not be necessary. In these cases, traditional rule-based systems or automation tools can provide efficient solutions without AI algorithms. Short-term or temporary projects: The time and resources required to develop and integrate AI systems may not be justifiable for short-term projects or initiatives with a limited lifespan. In such cases, focusing on simpler and quicker solutions that meet the project's specific requirements may be more practical.
- 26. Fintech Startup Environment in Pakistan The fintech industry has brought about various advancements various advancements that have led to the development of processes like mobile payments, P2P lending, solutions based on blockchain technology etc, The sector currently holds a significant 2% share of the global economy, and it is estimated to generate an impressive $1.5 trillion in annual revenue by 2030, which would account for almost 25% of all high-value industry sectors.
- 27. The Fintech Landscape In Pakistan In 2001, only a tiny fraction of the population, specifically 1.3%, had access to the internet. However, by 2012, it had grown to 10.0%. As of July 2021, approximately 118 million citizens in Pakistan now have access to the internet, which accounts for 54% of the population. Notably, the mobile penetration rate has surpassed 77%, indicating a significant increase in mobile phone users. This shows that if provided with resources, fintech can be a booming industry in Pakistan.
- 28. Challenges Faced By Fintech Startups In Pakistan Market Saturation And Limited Investment The fintech market is already filled with existing organizations. These organizations are not always welcoming of innovation, which is an issue for upcoming fintech companies trying to find partnerships or investments. Most of the fintech services in Pakistan are owned by well-established banks or telecommunications companies, almost all funding and investments are directed toward them, leaving fintech startups with limited Unreliable Infrastructure The digital infrastructure in Pakistan also needs to be improved. A clear example is the nationwide internet blackouts, which make internet services in Pakistan unreliable. This uncertainty and unreliability of the internet can disrupt transaction processing and service delivery. Payment services like Paypal are also nonexistent in Pakistan, which shows the massive lack of digital payment infrastructure in Pakistan’s cash-based economy.
- 29. Challenges Faced By Fintech Startups In Pakistan Regulatory Failures Despite implementing regulations such as the Regulations for Mobile Banking Interoperability and creating the Third Party Service Provider (TPSP) License that highly favor fintech startups, not all policies by Pakistan's government are friendly to fintech advances; a clear example of this is the 2018 ban on cryptocurrency trading and mining.
- 30. KEY PLAYERS, PRODUCTS AND TRENDS Category Key Technologies Real-life examples Payments Mobile payments, smartphone wallets, digital wallets, acceptance devices, Central Bank issued digital currencies Apple Pay, Android Pay, PayPal Mobile Express Checkout, Venmo, Square, TransferWise Insurance Online insurance platforms, online brokers, peer-to-peer insurance insureon, Knip, Oscar, Slice, Cuvva Investment advice Robo-advisory, digital wealth management, digital investment tools, tailored financial messaging Motif, Wealthfront, Nutmeg, Juntos
- 31. Factors Driving Fintech Revolution Increased Mobile Usage One of the biggest drivers of Fintech growth is the increased use of mobile devices. In fact, a recent study by Pew Research Center found that over 77% of American adults now own a smartphone. This has led to a surge in mobile banking, as well as other mobile-based financial services. For example, there are now mobile payment options for nearly every type of transaction, from credit cards to shopping online. In fact, according to a recent report by Business Insider, people in the U.S. spent over $100 billion through their phones last year alone The Rise in Digital Payments. Another factor driving the growth of fintech is the increasing popularity of digital payments. This can be seen in the rise of mobile payment options, as well as other forms of online and electronic payments. For example, a recent study found that the global value of digital payments grew from $5058.96 billion in 2020 to $5872.89 billion in 2021.
- 32. Factors Driving Fintech Revolution Focus On Underserved Areas of Banking: Banks have been focusing on certain areas of their businesses that have seen a lot of growth in the past, such as wealth and asset management. At the same time, there are other areas that have been neglected due to this focus which fintech companies are now trying to move into such as small business lending, student loans, and mortgages. APIs Fintech companies also use APIs (application programming interface) to build their products and services around existing financial infrastructure which has become a new trend.
- 33. Computer science and its applications to businesses Computer science plays a pivotal role in businesses across various industries by enabling automation, data analysis, efficient communication, and enhanced decision-making. Here are some key applications of computer science in businesses. 1. Data Analysis and Business Intelligence Businesses generate enormous amounts of data. Computer science provides tools and techniques to gather, store, process, and analyze this data to derive valuable insights. Business intelligence systems help in making informed decisions, identifying trends, and future outcomes. 2. Automation and Process Optimization Computer science enables businesses to automate routine tasks and processes, which increases efficiency and reduces errors. Robotic Process Automation (RPA) and workflow management systems are examples of how businesses can streamline operations.
- 34. Computer science and its applications to business 3. E-commerce and Online Presence Online retail, e-commerce platforms, and digital marketing are heavily dependent on computer science. Algorithms recommend products, websites provide personalized experiences, and online transactions are secured using encryption and authentication methods. 4. Customer Relationship Management (CRM) Computer science powers CRM systems that help businesses manage customer interactions, track customer behavior, and personalize services. This leads to improved customer satisfaction and retention.
- 35. Computer science and its applications to businesses 5. Supply Chain Management Computer science plays a vital role in optimizing supply chain operations through algorithms that manage inventory, demand forecasting, logistics, and distribution, resulting in cost savings and better resource allocation. 6. Finance and Fintech Financial institutions use computer science for high-frequency trading, risk assessment, fraud detection, and algorithmic trading. Fintech startups leverage computer science to create innovative payment systems, digital wallets, and peer-to-peer lending platforms. 7. Healthcare Informatics Electronic Health Records (EHRs), telemedicine, and medical imaging technologies are all built on computer science principles. These technologies enhance patient care, streamline workflows, and enable better data-driven decisions for healthcare providers.
- 36. Computer science and its applications to businesses 8. Manufacturing and Industry 4.0 Computer science contributes to the concept of Industry 4.0, where automation, Internet of Things (IoT), and data analytics are integrated to create smart factories. This leads to efficient production, predictive maintenance, and optimized resource utilization. 9. Logistics and Transportation Computer science aids in route optimization, real-time tracking, and predictive maintenance of vehicles in the logistics and transportation industry. This results in cost-effective and timely deliveries. 10. Virtual Reality (VR) and Augmented Reality (AR) These technologies are used for employee training, product design, and interactive customer experiences. They provide immersive and engaging ways to interact with products and services.
- 37. Computer science and its applications to businesses 11. Energy and Sustainability Computer science assists in managing energy consumption, optimizing energy grids, and simulating environmental impact scenarios. It contributes to more sustainable business practices. 12. Market Analysis and Prediction Computer science algorithms analyze market trends and predict future patterns, helping businesses adjust their strategies accordingly.