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Five ways to empower ‘Gig Workers’ with Digital Lending Solutions

The “Gig Economy” has emerged as an increasingly relevant phenomenon in today’s job market. The work model allows professionals to offer their services independently, especially through digital channels, without being tied to traditional job roles. This method offers flexibility and autonomy, simultaneously providing the opportunity to diversify incomes and explore different areas of expertise.

By Ranjan Kumar, Head of Finance & Accounts, RupeeRedee

Ranjan Kumar, RupeeRedee
By Ranjan Kumar, Head of Finance & Accounts, RupeeRedee

With the advancement of technology and the appearance of a myriad of digital platforms, the Gig Economy has attracted a significant number of professionals. Currently, it is expanding at a CAGR of 15% and fostering a robust network of workers ranging from delivery workers, drivers, designers, programmers, and many others.

However, regardless of the nature of work, the lack of financial stability remains a fundamental constant, placing Gig workers in need of robust financial services. The burgeoning potential of this economy space demonstrates the benefits fintech can avail by tapping into this new labour paradigm, offering tailored financial solutions based on the needs of freelancers.

Why Gig workers, specifically?

Gig workers, even though they comprise 85% of India’s workforce, have irregular cash flow and limited access to financial products like credit cards or pre-approved credit lines, and any sudden expenditure can upend their stability.

Income and Wealth Management

Unlike salaried workers, gig workers are subjected to an uncertain flow of income, regular payment delays, or no employee-sponsored retirement or insurance plan to fall back on. Thus, they need to be offered financial services that systematically analyse and offer insights into their income patterns, incorporate fractional savings in their spending patterns, and provide them with education and awareness for the same.

Unique Financing solutions

Due to the unique nature of income patterns, gig workers appear as less credible than salaried workers, which leads to financial products like loans and credit cards being underserved to this segment by financial service providers. Therefore, there is a huge unmet need for hassle-free, low-interest credit, which can be given by employing tools that can assess the creditworthiness of gig workers tailored to suit the nature of Gig work.

Fintechs catering to the financial needs of freelancers

Although the Gig Economy is growing, there is still limited competition in terms of financial services, which provides a unique opportunity for fintechs to position themselves as leaders in this rapidly growing market segment. By focusing on providing tailored financial solutions like specialised bank accounts, financial management tools, and flexible lending options, they can deliver exceptional customer experience earning the trust and loyalty of gig workers.

Data Analysis and Profiling

Fintechs use leading technologies like AI and data analytics to assess credit risk in a holistic manner and gather data that allows them to understand the financial needs of this segment and provide inclusive and equitable financial services to workers in the Gig Economy.

Fintech-powered Tailored Products or Services for Gig Economy Professionals

Considering the scenario of the gig economy, new-age digital lending platforms offer low-installment-based loans that allow borrowers to not worry about immediate repayment and can, in fact, enjoy the flexibility of splitting it over a few days, weeks, or even months. Hence, they still have access to liquidity. In addition, digital lenders leverage business process management systems to automate and optimise internal processes related to the care and support of gig workers by adopting machine learning algorithms that give insight into their financial behaviour.

Furthermore, by implementing ECM systems, digital lenders can easily store, access and organise relevant information, maximising operational efficiency and ensuring data security and confidentiality of gig employees. Apart from this, in order to save money or generate a financial surplus, they offer to store money in an investment instrument at minimum rates that can be liquidated on short notice. Thus, fintech can capture an expanding market and build strong relationships with this new segment.

Future Venture

The future of the Gig Economy holds limitless potential with the development of intuitive interfaces designed specifically for the needs of gig workers. This involves offering income and expense tracking tools, providing clear reports on transactions, and providing access to relevant financial resources, which poses an incredible venture for financial service providers to attract and retain Gig Workers.

CategoriesAnalytics IBSi Blogs IBSi Flagship Offerings

Embracing technology to navigate economic turbulence in the financial services sector

Guy Mettrick, VP, Financial Services at Appian
Guy Mettrick, VP, Financial Services at Appian

Today’s dynamic financial landscape has exposed the vulnerabilities of the financial services sector and shattered preconceived notions about banks’ regulatory resilience. The rapid collapse of once-revered institutions highlights the fragility of the banking sector in the face of economic turbulence and unforeseen market shifts.

With analysts scrambling to dissect the factors behind these failures, it is crucial to consider the broader implications for the financial services industry and the potential ripple effects on the overall economy.

Guy Mettrick, VP, Financial Services at Appian

Adaptive strategies for growth and innovation are becoming increasingly important amidst a background of stricter risk management, reduced lending, and increased regulation. To navigate the unpredictable path ahead that is defined by tightening regulatory frameworks and resource limitations, agility is key.

Balancing regulatory challenges

Mounting regulations driven by factors such as climate change and the push for enhanced compliance are forcing businesses leaders to reconsider their organisation’s strategic approach. The prominence of environmental, social, and governance (ESG) objectives in the financial services sector requires increased attention and significant investments in human resources and technology.

While these circumstances may lead to scaled-back growth aspirations, cost-cutting initiatives and deferred investment decisions, they also present transformative opportunities.

Leveraging technological advancements

During economic uncertainty, technology emerges as a powerful force within the financial services landscape. When it comes to expediting client onboarding, enhancing customer service, and facilitating seamless communication between financial institutions and their clients, automation proves indispensable. Automation enhances process efficiency and efficacy by eliminating manual tasks and minimising errors. Advanced technologies like artificial intelligence, robotic process automation, and process mining empower financial organisations to drive innovation within complex frameworks.

With automation, firms can facilitate real-time reporting and audits that provide tangible evidence of control effectiveness by embedding risk controls directly into their processes. In an era of increasingly stringent regulatory frameworks, this proactive approach to compliance proves invaluable.

The rise of data fabric

One emerging trend is the adoption of enterprise-wide data fabric, project by Market Watch to grow from $1.71 billion in 2022 to $6.97 billion by 2029. Data fabric streamlines the consolidation of data from various systems, a process that has traditionally been challenging and costly. This integration eliminates the need for data migration – a critical prerequisite for successful process automation.

Data fabric seamlessly connects and harmonises existing databases. This breaks down data silos and enables a cohesive and compliant framework that consolidates all relevant data sources. Within the financial services sector, this technology facilitates easy access to vital components such as risk governance policies and customer data.

Financial service providers must adopt adaptive strategies and embrace technology to effectively manage risks, regulations, and growth during an economic downturn. Regulation should not be perceived as a burden. Financial institutions should view technology, particularly process automation, as a catalyst for growth. Automation and data fabric enable these organisations to navigate complexities, streamline operations, and enhance customer experiences. Rather than succumbing to challenges, financial service providers can leverage technology to foster innovation, ensuring resilience in the face of economic uncertainty.

CategoriesAnalytics Artificial Intelligence IBSi Blogs IBSi Flagship Offerings Loans

Specified user FinTechs are helping lenders ride the AI wave for origination and underwriting

Raman Vig and Sudipta K Ghosh co-founders of Roopya
Raman Vig and Sudipta K Ghosh co-founders of Roopya

The Indian digital lending industry is undergoing a major transformation due to its unprecedented pace of growth. As per the recent stats – more than 200m people have availed of retail loans in a year and this is growing at 20% CAGR.

By Raman Vig and Sudipta K Ghosh co-founders of Roopya

The significant rise in the disbursement volume not only exhibits the uptick in the number of borrowers but also demonstrates the emergence of digital lending players in the market.

Many FinTech companies are overshadowing brick-and-mortar lending institutions by digitising every aspect of the lending process. This can be attributed to the rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) models that expedite and enhance the lending process. Given the scenario, the new-age lenders are moving from traditional risk models to a data-backed approach to be more relevant in the market.

A major step towards addressing gaps in the lending ecosystem

Data is the most critical element for any AI / ML model. In lending, credit bureau data and alternate data becomes the base for any propensity model for loan origination, preparing scorecards for underwriting, or even creating early warning signals on existing portfolio.

Hence data becomes the most powerful and significant force that drives the digital lending industry. In the present ambiguous scenario, the Indian lending industry has flagged several concerns on the dynamics of the data distribution of borrowers among lenders.

India has more than 1200 active lenders, out of which, only 1% have access to advanced data and analytics tools. This creates a significant gap on the supply side as small and mid-sized lenders lose out on the data-driven lending race. The new-age loan origination and underwriting tools which are accessible only to large-sized lenders create a huge disparity in data intelligence. Consequently, these lenders have to incur high acquisition and underwriting costs, ultimately leading to high-interest rates for borrowers.

Grappling with an unregulated lending scenario, the Reserve Bank of India (RBI) planned to put a guardrail on the ecosystem. The apex bank announced the appointment of a new set of FinTech companies as ‘Specified Users’ of Credit Information Companies (CICs) under the Credit Information Companies (Amendment) Regulations Act, 2021 based on stringent eligibility criteria. These Specified User FinTechs get access to credit data, run analytics and help digital lenders make data-driven decisions.

The appointment of Specified User FinTech players has not only regulated credit data distribution but also resulted in more streamlined and secure digital loan processing.

AI underwriting models

Every year, over 15 million ‘New to Credit’ borrowers enter the credit ecosystem. This makes loan underwriting a tricky process for lenders under the existing conventional models. Every customer or borrower has unique financial circumstances which bring uncertainty many inches closer to making credit decisions.

If an underwriting practice is not backed by data and analytics, it can lead to economic meltdowns for lenders. And that’s where Specified User FinTechs come to the rescue, providing lenders with the ability to interpret enormous data amounts much faster and more accurately than conventional underwriting practices. It equips lenders with AI and ML-backed underwriting models, adding an extra layer of better oversight on how data sets can be used strategically to come up with personalized solutions for each borrower.

FinTech players are one of the early adopters of technology. The advent of Specified User FinTechs helped lenders to venture into segments that were deemed high-risk by conventional lenders. Simply put, they have been successful in bridging the accessibility gap for underserved lenders, making them ride the wave of AI.

Predictive algorithm to streamline the lending process

In practical terms, AI works intuitively like predicting defaulted or paid loans. Specified User FinTech combines AI algorithms with ML classification mechanisms to create probability models for lenders to have better credit decision ability. The technologies are applied to improve credit approval, and risk analysis and measure the borrowers’ creditworthiness, which further helps small and mid-sized lenders scale with ease.

FinTech companies that are recognized as Specified Users have competencies to store huge amounts of credit data and build AI and ML models on structured and unstructured data sets. This provides more streamlined and better insights for borrower segmentation, predicting loan repayment, and helping in building better collection strategies. Besides this, Specified User FinTechs are helping lenders to be on top of automation whether in loan underwriting or pricing for personalized offerings.

On a similar backdrop, lenders’ ability to recognize early warning signs proves to be highly beneficial for lenders with credit risk management. Recognized by RBI, lenders can be certain of the credibility of Specified User FinTechs in terms of data and analytics.

Specified User FinTechs leverage the intuitive yet data-backed behavior that detects any suspicious borrower and red flags as fraud. Unlike traditional tools of analysis, it can alleviate the possibility of human errors arising from biases, discrimination, or exhaustive processing practices. By utilizing NLP (Natural Language Processing), lenders can accurately generate warning signals instantly.

Final Thoughts

The landscape of digital lending in India is continuing to evolve. Lenders can reap the benefits of data hygiene performed by AI and ML infrastructure established at the Specified User FinTech’s end. By automating and bringing all significant practices to one place, lenders are empowered to improve customer experience, take leverage of predictive analysis, enhance risk assessment, and improve credit decisions and breakthrough sales bottlenecks.

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