The “sharing” economy is a socio-economic ecosystem built around the sharing of resources, such as property, services, or skills, with others for a fee or exchange. This model is facilitated by online platforms that connect providers of these resources with users who need them. The concept has radically transformed a wide range of industries, including finance, hospitality, transportation, and freelance work, and is continuing to grow and evolve as new platforms and services emerge. This blog post explores the key features of the sharing economy (1), analyzes the challenges it raises (2), and provides insights on how data scientists can navigate this landscape effectively (3).
1. Key Features: More Ressources, More Savings
To better understand the other key features of the sharing economy, we’ll focus into the fintech sector which has revolutionized the financial sector by leveraging technology to create new financial services and products that provide greater efficiency, convenience, and accessibility.
Create accessibility. As highlighted in the introduction of this article, the core objective of the sharing economy is to maximize the efficient use of idle assets and resources. The innovative drive of its participating entities centers on rapidly adapting to evolving market demands and user needs for more accessibility and inclusivity. Fintech sharing platforms aim to include previously underserved populations, offering financial services to those who may not have access to traditional banking. Back in 2007, Vodafone collaborated with Safaricom to launch M-Pesa, the first mobile phone-based service of money transfer, payment and micro-financing in Africa. Next, crowdfunding websites like Kickstarter and Indiegogo allow individuals to fund projects or ventures by raising small amounts of money from a large number of people, breaking down barriers in accessing capital. Finally, Gig economy platforms like Uber or Deliveroo base their model on flexible, temporary and freelance jobs. They connect clients (independent contractors and freelancers) with customers and manage their payments and financial resources. This allows individuals to earn extra income and can provide more affordable and flexible options for consumers.
Reduce costs. The sharing economy also capitalises on its ability to reduce costs for both service providers and users mostly through technological advancements like AI, IoT, and blockchain. Peer-to-Peer (P2P) transaction platforms like LendingClub and Zopa enable direct financial transactions between individuals, connecting borrowers directly with lenders and bypassing traditional banking institutions. This democratizes lending and borrowing, making it more accessible and often more cost-effective. Blockchain technology, exemplified by cryptocurrencies like Bitcoin, also represents a shift towards decentralized financial systems where transactions occur without central authority. Another example is Lemonade, a digital insurance company that uses AI and machine learning to provide fast, personalized insurance policies to customers and allows them to file claims and receive payouts quickly and easily through its app. Its approach decentralizes and streamlines traditional bureaucratic structures and processes in insurance, offering a more direct, user-friendly approach.
Build trust. Another key feature of the sharing economy is the ability to build trust and reputation through user ratings and reviews. These ratings and reviews can help providers establish a reputation for reliability and quality, while also providing consumers with information to make informed choices. Consequently, the increased acceptance and trust in shared economic models have been pivotal in driving the market capitalization of top companies to double, reflecting a significant surge in their valuation and investor confidence.
2. Key Challenges: More Regulation, More Competition
The sharing economy, while innovative, navigates a minefield of challenges that could impact its sustainability and growth.
Complex regulation. Regulatory complexity stands out as a primary hurdle, with varying laws and standards across different regions creating a complicated landscape for these businesses to operate within, affecting their global operations and expansion strategies. Cryptocurrency platforms, like Coinbase, operate in a rapidly evolving regulatory environment. Different countries have varying stances on cryptocurrencies, from outright bans to welcoming regulatory frameworks. As a result, in March 2021, Coinbase faced regulatory challenges in India. The company had to delay its planned launch due to uncertainty around India’s cryptocurrency regulations. At that time, the Indian government was considering a bill that could have banned private cryptocurrencies in favor of a state-backed digital currency.
Data breach. In recent years, the spotlight on breaches of consumer personal information has intensified, significantly heightening concerns around data security and privacy. This trend is gradually eroding user trust, a critical component of the sharing economy’s success. High-profile incidents, such as the 2018 Facebook-Cambridge Analytica data scandal, where millions of users’ data were harvested without consent, serve as a stark reminder of these vulnerabilities. In 2019, a major breach affected Capital One, exposing the personal information of over 100 million customers. This incident not only raised concerns about data security in fintech but also eroded user trust, highlighting the vulnerability of even large, established financial institutions in safeguarding user data.
Volatile trust. A crucial aspect of the sharing economy’s model – the trust system built on user ratings – also faces potential pitfalls. Over-reliance on this system can lead to inconsistencies in service quality and reliability. For example, in platforms like Uber or Airbnb, a few negative experiences, amplified by social media, can quickly undermine the perceived reliability of these services. This scenario highlights the fragility of a reputation-based system in an era where consumer opinions are instantly and widely shared online.
Market saturation. Furthermore, the sector’s rising popularity has led to a fiercely competitive environment, risking market saturation. As more players enter the space, distinguishing oneself becomes increasingly challenging, and consumers face an overwhelming array of choices. The online payment sector within fintech has become highly competitive, with numerous players like PayPal, Stripe, and Square. This saturation forces companies to continually innovate and differentiate themselves to maintain a competitive edge, as seen with PayPal’s expansion into cryptocurrency services.
3. Data Scientists: More Sustainability, More Ethics
Data scientists play a crucial role in ensuring the sharing economy’s strengths and opportunities are maximized while mitigating risks and threats. While data science can provide powerful insights, these need to be tempered with a sustainable approach, ethical considerations and strong governance.
Innovation and demand. First, data scientists must adapt to evolving market demands and user needs by leveraging vast data to understand consumer behavior, forecast trends, and tailor services, continuously exploring new ways to apply data science in enhancing the sharing economy’s model, such as through improved payment systems or risk assessment algorithms. A good demonstration of the effectiveness of using data-driven strategies to adapt to market needs is Block (previously Square) that initially started as a payment processing service but expanded its offerings based on market trends and customer feedback. Indeed, their data scientists analyzed transaction data and customer behavior to identify the need for additional services like business loans (Square Capital) and payroll services. Another interresting innovation is the development of Sesame Credit that uses a combination of Alibaba‘s vast e-commerce data and other sources to assess an individual’s creditworthiness. This model goes beyond traditional credit scoring and considers a variety of non-traditional factors, such as online shopping habits, bill payments, social network behaviors, and more. Millions of individuals and small businesses in China were then allowed in 2020 to access credit and financial services, which were previously unavailable due to the lack of a traditional credit history.
Sustainable model. Another contribution from data scientists to the sharing economy is to evaluate the long-term sustainability of their strategies by limiting the over-reliance on data models and establishing adequate checks and balances. Many fintech companies provide innovative financing sources, using data science to improve their credit risk models. By analyzing a vast array of data points, their data scientists develop models that more accurately assess the creditworthiness of applicants. On the paper it is aimed at minizing the risk of default but also allowing to offer credit to a broader range of customers, including those who might be underserved by traditional banks. The reality is mitigated by the skills of the data scientist teams involved. LendingClub, introduced earlier, pushed the exercise up to becoming a peer-to-peer lending platform and connected borrowers directly with lenders outside of traditional banking systems. Their issues arose in 2016 when it was found that some loans were not as risk-averse as their data models had suggested.
Ethical algorithm. Ensuring AI and machine learning models are unbiased and ethical, particularly in credit scoring and risk assessment is another key data scientists duty. ZestFinance‘s AI and machine learning technology is designed to analyze vast amounts of data to assess the creditworthiness of individuals, particularly those with little to no credit history in order to help lenders make more accurate credit decisions. In 2019, the company was part of an investigation by the U.S. Department of Justice under the Fair Housing Act. The concern was about the algorithms potentially leading to discriminatory lending practices. The use of complex machine learning models in credit scoring inadvertently resulted in biases against certain groups, especially as the data fed into these algorithms reflected historical biases. ZestFinance data scientists, acknowledging these challenges, shifted their focus towards developing a more transparent AI model for credit scoring baptised ZAML Fair™. This involved rigorous testing and continuous monitoring of the models to identify any inadvertent biases and to validate that the credit decisions were socially fair and compliant with social regulations. They also began emphasizing the importance of “explainable AI” in lending, to ensure that decisions made by their algorithms could be understood and justified in human terms.
Regulatory compliance. Data scientists are also expected to oversee regulatory compliance. Assisting in navigating complex regulatory landscapes through data-driven insights can have either positive impacts on the society. Like when Data scientists at Stripe developed a sophisticated algorithm known as “Radar” analyzing millions of transactions, identifying and flagging potential fraud hence saving millions of dollars and maintaining user trust. However, it is much easier to find illustratons of negative impacts. Playing with the regulations loopholes to support high-frequency trading is unfortunately the most negative one. Not only does it weaken the financial market but it also redistributes its ressources to the wrong players.
Data security. Finally, data scientists should take a holistic approach to data security and include not only advanced data protection algorithms but also regular system maintenance and updates to their research and applications. While a data incident shall never be a direct failing of the data analytics department, making sure they properly collaborate with the cybersecurity analysts can ensure their personal investment do not go to waste after a scandal like for Equifax in 2017. One of the largest consumer credit reporting agencies suffered a massive data breach due to a failure in updating their software to patch a known vulnerability which exposed the personal information of about 147 million consumers.
Explore more
Emanuele Crisostomi, Bissan Ghaddar, Florian Häusler, Joe Naoum-Sawaya, Giovanni Russo, Robert Shorten (2020) Analytics for the Sharing Economy: Mathematics, Engineering and Business Perspectives
Arun Sundararajan (2017) The Sharing Economy: The End of Employment and the Rise of Crowd-based Capitalism
New York Times (2020) Is an Algorithm Less Racist Than a Loan Officer?