The world is one big data problem. Every human being generates around 1.7 megabytes of data every second. This information can help generate economic benefits by transforming this data into new revenue streams by extracting better insights to design tailor-made financial products and streamline risk management.
Benefits of data monetisation
Data monetisation is unfolding new opportunities in revenue generation. Today, cash flow-based loans help micro, small and medium enterprises (MSMEs) access credit without collateral. Rural lending is also shifting toward cash flow-based lending, which lowers costs and attracts big banks and financial institutions. Fintech solution providers are bringing data, artificial intelligence and machine learning to banks to help them better assess credit risk.
Farmer data on KYC, geo-coordinates of farms, history of crops they have sown, crop size, yield and potential earnings factor into the partner bank’s digital platform. This information is collated with remote-sensing data to predict a farmer’s productivity, yield estimates and selling price.
The final step is plotting the farmers’ risk scores using a machine-learning algorithm. Banks can make instant
credit disbursal to clients in rural areas by assessing the cost of input/output, positive cash flow and profitability.
After the loan issuance, the bank conducts remote monitoring and evaluation using satellite imagery based on periodic data. It provides information on whether the farmer has used the disbursed loan for the intended purpose. The bank is alerted to contact the farmer to initiate the repayment process when the crop approaches the harvest stage.
Data monetisation also increases the value of the data. Insurance companies offering crop protection to smallholder farmers can leverage such technology for underwriting and claims administration. In the dairy sector, insurance firms measure cash flow based on the quantity and quality of milk produced to determine the amount of compensation payable to livestock farmers from loss of income due to extreme temperatures in summer months when milk yield falls drastically.
Key elements of an enabling digital technology ecosystem
Data monetisation requires an adequately developed technological infrastructure and is best considered in the broader context of digital financial inclusion, driven by needs, objectives and capacity rather than technology. Additionally, strong commitment by financial regulators and governments, and trust in the data is critical for its acceptance.
Big data privacy risks and consumer concerns
With big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway that can invade personal privacy in multiple ways. And one of the unintended consequences can be financial exclusion.
Using data in predictive analytics to make decisions can negatively impact individuals and lead to discrimination – for example, not providing health insurance to a person with a family history of cancer.
There is heightened reputational risk to financial institutions due to identity theft and exposing personal information due to data breaches or the loss of anonymity.
The role and importance of information management and governance in data privacy and allaying consumer concerns will be a crucial success factor in instilling trust.
Data security mechanisms
Several techniques and methods are in vogue today to strengthen data security. Fingerprints, face recognition, user names and passwords are some primary authentication measures. In addition, encryption, masking, tokenisation and hashing can prevent hackers from acquiring any sensitive information as the stolen data becomes entirely useless for the attackers.
Securing data is more important than the data itself. So, the implementation of these data security measures can provide confidence to monetise your data.
SupTech will improve supervisory processes
SupTech and Regtech tools could have significant benefits for financial stability. For regulatory authorities, the use of SupTech could improve oversight, surveillance, analytical capabilities and generate real-time indicators of risk to support forward-looking, judgment-based supervision and policymaking. For example, the use of SupTech for ‘misconduct analysis’ and micro-prudential supervision has increased in recent years.
RegTech will improve regulatory compliance
The use of RegTech by regulated institutions can enhance risk management capabilities, improve compliance outcomes and help generate new insights for improved decision-making.
Preparing for safe data monetisation
Data monetisation can be complex, and anything that’s not simple is bound to spark worries and anxieties. As a result, privacy concerns grow as governments enact regulations and laws to deal with the problem.
And, if you are the head, whether of a financial institution or a regulatory agency, you don’t want headline news.
Every organisation needs to have a data monetisation strategy that can run the entire spectrum. This strategy is essential because:
Perceived fear is higher than actual risk. However, once an organisation recognises the complexities of compliance and regulatory requirements, monetising data is not as scary as many people make it to be.
Not all data is created equal. We can have data without information, but we cannot have information without data. As the renowned economist Ronald Coase said, “Torture the data, and it will confess to anything.” There are three types of data:
- Raw data with Personal Identifiable Information (PII): A lady factory worker walks into a remittance kiosk at 10:52 am and pays $200 for a remittance. Also included: credit card number, address, email, or maybe her date of birth, or – depending on the circumstances her social security number. This data type has a high level of risk but is also valuable, so you need to be careful with it.
- Anonymised data: A woman in Dubai walks into a remittance kiosk at 10:52 am and pays $200 to remit to her family. Once this data set is scrubbed of any information that would point back to the lady factory worker, she can no longer be identified, and hackers cannot steal her sensitive information. Yet even without these details, the data remains useful in many ways that raw PII is valuable – simply with much less risk.
- Synthetic data: A woman in Dubai walks into a kiosk at 10:50 am and pays $201 for a remittance. This is an example of a fake data set created from the core data. The numbers are close enough or statistically relevant to use for most legitimate purposes – that is, analytics – but they are synthetic or falsified, making it impossible to reverse engineer the transaction back to either an anonymous person or an actual, identifiable customer.
Synthetic data, of course, carries a shallow level of risk around its use, while anonymised data is slightly riskier, and raw PII is the riskiest.
The compliance and regulatory structures and data monetisation opportunities are specific to each data type. Therefore, one must be intimately familiar with the compliance and regulatory standards around doing so, with a well-articulated privacy structure in place to mitigate those risks.
Data management matters a lot. Machine learning (ML) and artificial intelligence (AI) can’t use messy data. It needs to be kept clean in a modern data platform such that ML can use it. The organisation could then build analytics for unique customer insights or sell additional analytics products based on its customers’ data back to those core customers.
Store it in outdated silos, and no intelligence—artificial or live—will stand a chance of finding what is needed. Hiding within those mounds of data is the knowledge that could change the life of a poor girl child. The goal is to turn data into information, and information into insight.
A three-pronged move
We are moving slowly into an era where Big Data is the starting point, not the end. There are three different channels to monetise data:
- Internally, data can be cleansed and then fed into a new ML system for a new purpose – say, analysing the organisation’s customers and its operations. The organisation could then build analytics for gaining unique customer insights.
- Externally, there are many ways that others might like to leverage this data. For example, say a bank wants to know where people shop an hour before and an hour after withdrawing cash from the ATM. A shopping mall is just one example of a third party that could benefit from data that the organisation has in its possession.
- Finally, innovation or AI/ML initiatives represent opportunities for organisations to think outside the box and do something genuinely innovative, creating brand new products based on the data. Think self-driving cars or healthcare data that enable providers to identify skin cancer before it becomes a more significant issue. Many financial institutions have introduced innovations like chatbots, digital assistants and payments fraud detection solutions.
Although data monetisation may look similar in design, they may work very differently in practice, depending on the epistemic beliefs of practitioners and the organisational context within which quantification takes place. Therefore, if data doesn’t support your belief, change your belief.
Suppose data enables organisations to manage the risks and the goals set and achieved. In that case, the upside of data monetisation can indeed be tremendous because of its immense power to ensure the highest possible social value capture with the lowest possible risk.
A version of this piece was published on LinkedIn, and can be read here. It has been republished on Unravel with permission from the author.
Arup Chatterjee is Principal Financial Sector Specialist, Sustainable Development and Climate Change Department, Asian Development Bank. His current work involves financial, governance, risk management, and regulatory reforms across different industries. He has held stints with the Bank for International Settlements in Switzerland, and Insurance Regulatory and Development Authority of India.