Digitalisation Archives

November 7, 2023December 18, 2023 byPriti Gaikwad

Unlocking a digital future: how the finance industry can improve data quality

Deloitte’s recent predictions for the future of finance highlight the need for the finance industry to adopt the technology available to them in order to remain competitive. But for the finance sector to become truly digital, the quality of data is paramount.

By Baiju Panicker, Global CTO and Practice Head – Banking, Insurance and Financial Services at Altimetrik

Shifting to a truly digital mindset means adopting a digital business methodology that uses data to support and improve operations. However, if the data is low in quality, incomplete, or corrupted, this will make it near-impossible for the business to operate in an efficient and effective way.

Data quality critical in finance

By Baiju Panicker, Global CTO and Practice Head – Banking, Insurance and Financial Services at Altimetrik

Low-quality or incomplete data can lead to poor lending, high-risk, flawed valuations and suboptimal trading. Ineffective targeting can also result from poor data, as can complaints, failures, and distorted insights.

In stark comparison, accurate data enables sound business decisions. High-quality data provides insight for analytics and efficient banking activities. It establishes greater integrity across operational analytics, fundamental to successful financial decisions and the overall success of the financial industry.

A great example of this is artificial intelligence (AI). AI is only as good as the data it accesses. It is crucial for financial firms to invest in data quality at the outset to enable successful digitisation, which in turn can boost competitiveness in the market and increase customer satisfaction.

Technology adoption critical

The adoption of technology is central to improving data quality. Leveraging various technologies to enhance data quality, such as automation tools for validation, AI for anomalies, and streaming analytics for real-time monitoring can ensure that only accurate and validated data is captured, improving the data quality immediately.

Data machine learning, blockchain, and natural language processing can help financial institutions improve their data quality and overall market performance by spotting inconsistencies, securing transactions, and extracting insights.

Without these building blocks, there is great potential for failure. Multiple client records can cause confusion, incorrect bills can damage trust, and customers and contracts may be lost.

Cleansing existing data is vital, but it is important to recognise that this cannot effectively be undertaken as a one-off project. Instead, it needs to be implemented as an ongoing activity to ensure overall business success. Alongside this ongoing process, businesses need to properly validate data as it comes in, such as automating data input and real-time monitoring to maintain a high standard of data throughout.

Utilising data stewards to monitor and address data quality issues gives a direct responsibility within the business to monitor data, clearly setting out the business’ intention to staff, customers and stakeholders that data quality is at the heart of the organisation and its operations.

Building a sustainable data quality framework

Undoubtedly, there will be lots of challenges that businesses face whilst undertaking this process. Focusing on a short-term goal – such as a single data cleanse – can be short-sighted and only create the same problems further down the line. Ensuring coordination across the business is key to success, which leads to greater accountability and removes silos from the process.

Machine learning and rule-based detection can support teams and help avoid any deviation from the prescribed style of data being captured. Text mining and natural language processing can help businesses analyse documents, call transcripts, and social media posts to identify semantic anomalies and outliers that indicate data quality issues. Alerts can then be set up to flag when issues emerge.

Ultimately, combining technology-driven detection with business-driven strategies for ongoing data quality improvement will enable businesses to be vigilant regarding poor quality or erroneous data being captured and utilised.

How to ensure quality

Proactive checking of data for errors and maintaining its quality is vital to the whole process of data quality, as early identification of problems helps to establish trust. Establishing a governance structure internally, where all parties are aware of and active in their roles, is fundamental both from a business perspective and also for stakeholders and customers.

Cross-functional data governance is important. It is not enough for each department to run its own checks and processes; it needs to be business-wide, with no silos or breaks in communication. This is where a Single Source of Truth (SSOT) is important. Rather than having multiple data locations that might not interact with other departments or processes, holding all the information centrally allows for better data accuracy and effective data cleansing across the whole business.

Overarching benefits of high-quality data

The potential benefits of improved quality of data to financial organisations are manifold. There is huge potential for increased revenue and cost savings through optimised data-driven decisions and operations. Data-driven activity is always more accurate, and data quality is central to this. The results are improved customer satisfaction and retention, with improved product offerings based on accurate findings.

From an operational perspective, management will see higher employee productivity with reliable data to work with, coupled with higher staff satisfaction. Through the integration of accurate, high-quality data there can be an increased use of automation and AI for more mundane tasks, enabling employees to work on more challenging and rewarding activity.

The finance industry is at a crucial juncture when it comes to digital adoption. Those who embrace digital adoption and intelligent ways of working through data and intelligent analytics will thrive, whilst those who lag behind will struggle to compete against competitors with a digital business mindset.

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October 21, 2023December 18, 2023 byJeet Vasa

Digital Banking: Prioritising Financial Inclusion

In recent years, digital transformation and the rise of FinTech technologies have made digital banking increasingly accessible. Now, there is a wide variety of digital services available as banks continue to focus on delivering the best, most convenient services to their customers.

By Hans Tesselaar, Executive Director at BIAN

There is clear momentum happening in online and digital banking, with 416 million active users of online banking in Europe alone, an increase from 398 million in 2022. This is reflected globally, with 170 million users in 2023 in Latin America, expected to spread to almost 198 million next year. Emerging technologies can support this expansion, but it’s the responsibility of the industry as a whole to ensure financial inclusion and economic growth for all, which is a priority amid this growth.

Digital inequalities caused by this shift must be addressed through collaboration and emerging technologies, an area where some developing countries are leading by example. The role of industry standards is also incredibly important when looking to better deliver digital services to all.

Counting on industry standards

We can look to the Union Bank of the Philippines as an excellent example of this. The extensive use of legacy technology within banks means the speed at which these established institutions can bring new services to life is often too slow and outdated. This challenge is also complicated by a lack of industry standards, meaning banks continue to be restricted by having to choose partners based on the ease and cost of integration. This is instead of their functionality and the way they’re able to transform the bank.

To truly digitise, banks need to overcome these obstacles surrounding interoperability with a coreless banking model. This approach to transformation empowers banks to select the software needed to obtain the best-of-breed for each application area without worrying about interoperability and being constrained to those service providers that operate within their own technical language or messaging model.

By translating each of that proprietary messages into one standard message model, communication between different parts of organisations is, therefore, significantly enhanced, ensuring that each solution can seamlessly connect and exchange data.

Adopting emerging technologies to increase accessibility

While some elements of financial inclusion and digital adoption require a more considered approach, there are instances where emerging technologies are bringing transformative services to the unbanked.

The Union Bank of the Philippines, for example, overhauled its quick loans retail engine (RLE) to serve as the central platform for the bank’s loan and credit products, leveraging its reusability and ease. Using a combination of low-code, based on the BIAN Models, and the adoption of BIAN APIs, the bank sought to establish a seamless, fully digital experience that could scale up to meet the country’s huge demands for loans by the unbanked.

This has enabled the Union Bank of the Philippines to overcome the issues preventing the RLE from scaling to the mass market to reach the 51.2 million unbanked Filipinos. Through this innovation, those who otherwise wouldn’t have access to a fully digital quick loan service now do.

This is just one example of many, as fintech adoption continues to grow in emerging markets due to the increasing use of mobile phones and the internet, the large unbanked population, and the growing middle class. It will be no surprise to see more of these examples where banks look to digital services to reach the mass market over the coming years.

Creating a supportive ecosystem

As FinTech adoption continues to grow in emerging markets, banks must form an ecosystem alongside fintech, service providers, and aggregators. This will help banks when it comes to the speed they can introduce new products.

An effective ecosystem strategy will make banks more relevant to their customers, providing an opportunity to drive better relationships and bigger wallet shares by providing the speed, scale, and differentiated products that make the most of the opportunity presented by the significant shift to digital banking. With this approach, banks can focus on offering services to meet the demand of all customers, whether that be digital, analog, or reaching the unbanked population.

The journey to digitalisation

To be truly inclusive, banks must assess their customer base and look to meet its needs.

Where digital adoption risks leaving customers behind, banks must ensure these customers are prioritised through collaboration, access to offline services, and a slow, steady digital transformation process. In other cases, digital transformation is the answer to bringing financial services to the mass market. In both situations, industry standards can be the key to unlocking new technologies and providing services to those who otherwise wouldn’t be able to access them.

Putting the customer first and taking a collaborative approach will be how the industry brings all customers along on the digitalisation journey. As long as the priority for banks remains on financial inclusion and innovation increasingly supports this, there will never be a customer left behind.

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October 21, 2023December 18, 2023 byJeet Vasa

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

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.

Digitalisation