Risk management challenges on Fintechs labor to financial inclusion
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Risk management challenges on Fintechs labor to financial inclusion

Desiree De Paul, Deputy Managing Director FINTECH 4F Sales Inc, 4Finance Group
Desiree De Paul, Deputy Managing Director FINTECH 4F Sales Inc, 4Finance Group

Desiree De Paul, Deputy Managing Director FINTECH 4F Sales Inc, 4Finance Group

The Fintech industry emerged as an opportunity to complement the highly competitive market of the banking and financial industry, with an endless source of technological innovations. In a world where the best customer profiles are disputed among market leaders, Fintech companies have made their way through the inclusion of sectors of the population historically marginalized by traditional banking. From the point of view of inherited risks, one of the biggest challenges these companies face, is how to predict the payment behavior on the mentioned communities, when in most cases, financial records either do not exist or information may not be easy to obtain.

The major cutting-edge tech tools to keep the wide range of risks under controlled levels apply Big Data and Machine Learning on Artificial Intelligence algorithms. The main application is on business processes automation to make more accurate and faster decisions in lending, scorecards modeling, risk management, fraud prevention, and many other areas.

Big data is helping the Fintech industry by transforming into hands-on insights the massive amounts of information collected from an increasing number of data sources, such as financial institutions, telephone companies, social media, mobile apps, and payment services. As traditional banking companies realized some time ago, the importance of exploiting computing force to reduce the usage of human judgment on lending decisions, Fintech companies have recognized the importance of transforming and exploiting alternative data to boost the predictive power of behavioral forecasting on their targeted populations.

 Big data is helping the Fintech industry by transforming into hands-on insights the massive amounts of information collected from an increasing number of data sources 

On the other hand, machine-learning models are designed to analyze and identify patterns in the data and to learn and incorporate the conclusions on its core algorithms for future assessments. The inverse correlation of in-house designed machine-learning models with the widely known scores available in the market has been extensively proved already, demonstrating the higher performance of this trendy instrument.

Another area where artificial intelligence is entailing a disruptive approach for risk management is the identity verification process. In the digital business, the probability of forgery and identity theft cases increases, therefore, more robust systems for document identification and facial authentication are indispensable to reduce the turnouts of a breach. Latest trends include advanced image processing systems for liveness detection on selfies pictures, visual reading technologies that scan personal data from pictures of relevant documentation (such as ID documents), voice recognition to detect suspicious behaviors like nervousness or stress, keystroke dynamics to identify patterns on typing, among others. Furthermore, the addition of these innovations on the registration funnel maximizes the validation accuracy while cutting minutes off compared to traditional identity-validation processing times.

The benefits arising from the application of these algorithms are evident and generous. However, the main assumption behind is that available data is representative of the whole society, leading to erroneous assumptions and negative implications on segments of the population that remain technologically relegated and financially marginalized. The referred contingency that exists on a worldwide scale suggests that negative consequences would be deeper in countries with a high scale of informal economy and unbanked groups and with low technology-penetration rates. In the end, it seems that human intervention is still a critical step on the set-up of logical-driven algorithms to contextualize the information that feeds the machine-learning models, to mix with judgment the definition of smart heuristics rules, and to collect the components of human nature like intuition, conscious reasoning and personal experiences on the filtering and processing of Big Data.

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