This article was brought to you thanks to The European Sting’s collaboration with the World Economic Forum.
Author: Laurent Le Moal, Chief Executive Officer, PayU, Naspers & Amelia Ng, Managing Director, SC Ventures, Standard Chartered Bank
- Worldwide, 1.7 billion people still do not have access to a formal bank account.
- Digital innovations like AI contribute to greater financial inclusion.
- With the right infrastructure, data sharing environment and ethical framework, AI can democratize financial services.
The volume of data generated worldwide is expected to increase by a whopping 530% – from 33 zettabytes in 2018 to 175 zettabytes in 2025. AI has the power to convert this flood of data into financial inclusion. We recommend several guidelines that the public and private sectors can apply to unlock the potential of AI while mitigating the risks of this relatively new technology.
Digital identification systems for financial inclusion
Governments and regulators have worked together to build a new digital identification infrastructure that will reduce the cost of reaching the last mile user. India is leading the way on this front by creating fundamental digital pipelines for its India Stack. Prime examples are Aadhar, a 12-digit unique ID issued by the Government of India, and UPI (United Payments Interface), an interoperable mobile payment system regulated by the Indian Central Bank.
While Aadhar enabled access to bank accounts by giving 1.3 billion people a trusted ID, UPI enabled cross-platform, instant payments for the first time. Together, these applications have fueled financial innovation by massively reducing transaction costs and enabling the collection and sharing of user data on a large scale.
The Indian fintech ecosystem – one of the most successful in the world – would not be the same without these central digital platforms that enable an open and free market.
Data portability and its application to the credit and finance sectors
Once the infrastructure is in place, data is the fuel that enables financial service providers to get the maximum benefit from it. Without this fuel, fintech will not be able to travel far, and without data portability there will be little fuel. Data portability refers to the user’s right to easily transfer personal data from one organization to another. In practice, this requires that data no longer be stored in an organization’s silos. Rather, it should be owned by users who can share it with other service providers. The underlying principle of this data sharing framework is that user-generated data is a public good that can and should be a source of competition and not a competitive advantage for any single party.
This principle of data sharing is demonstrated in India by the Account Aggregator (AA) framework. AAs are designed as “consent managers” that enable users to share their data – on a consent basis – with other regulated entities such as banks, insurance companies, etc. SME sector.
AI, machine learning, technology
How does the forum help governments adopt AI technology responsibly?
The World Economic Forum’s Fourth Industrial Revolution Center has worked with the UK government to develop guidelines for more ethical and efficient government procurement of artificial intelligence (AI) technologies. Governments across Europe, Latin America and the Middle East are piloting these guidelines to improve their AI procurement processes.
In addition to serving as a practical reference guide for governments looking to adopt AI technology, our guidelines set out basic standards for effective, responsible public procurement and the use of AI – standards that can eventually be adopted by industry.
We invite organizations interested in the future of AI and machine learning to take part in this initiative. Read more about our impact.
Around 90% of the country’s SMEs do not have access to formal credit. With the AA framework, these small businesses can ensure that lenders are provided with the necessary documentation, which in turn can better secure and serve small businesses. Thanks to a well thought-out system design, the AA framework enables the consent-based exchange of user data, reduces transaction costs and enables financial inclusion.
Guidelines for developing ethical AI
Just as important as infrastructure and data are the AI capabilities themselves, which determine the penetration of fintechs. While a wealth of data opens up new possibilities, the beneficiaries are limited to those for whom data is available. If the underserved are not adequately represented or the underlying algorithm is skewed, the AI can end up replicating social biases. This possibility is reinforced by the fact that there are currently no laws regulating AI algorithms. In such an environment, we urge private sector actors to take matters into their own hands and not wait for regulation to ensure that their use of AI is fair. We give three tips for this:
1. To reduce the risk of bias, increase the diversity of your teams
Columbia University researchers found that while the main cause of skewed predictions is skewed data, engineering demographics also play a role. While everyone was individually biased, homogeneous teams reinforced individual biases. The more diverse the team, the better equipped it is to recognize and reduce prejudices.
2. Make sure your AI algorithms are explainable to others, including experts and laypeople
Researchers at the Oxford Institute have developed the term “counterfactual”: counterfactuals do not provide a full explanation, but rather offer the minimum conditions that would have led to an alternative decision. Knowing these conditions can pave the way to inclusion by providing users with the information they need to change their current state.
3. Assign experts to check your algorithms
There are several frameworks to help those who wish to incorporate ethics into their development cycle. Instead of reinventing the wheel, get developers and experts of these tools on board. If expert involvement is not an option, ensure that human intervention is not completely removed from your decision-making process. As AI algorithms get better, human judgment remains more sophisticated and should be used to test and develop fairer algorithms.
With the right infrastructure, data sharing environment, and ethical framework, AI can be used to provide financial services to more people, especially the poorest. This possibility must be turned into reality.
For more information, please visit: corporate.payu.com