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Are Banks the Next Big Data Giants?

Banks, as we see today, are fast leveraging the latest technology to be the best in the financial services market. Traditional banks often deal with too much data and find it difficult to declutter, structure, and analyze the vast database from their legacy systems. They increasingly feel the need to leverage data as efficiently and effectively as possible.

Big Data Lead Transformations for Banks

With the use of big data, banks are capable of guiding the customers and help them navigate through major financial decisions flawlessly. Banks can use customer data to make personalized recommendations for their clients with the use of AI and ML technologies. Furthermore, banks can also get their clients in touch with third-party service providers like utility vendors and expand their areas of functioning. With big data, banks are embracing new digital business models that are inherently diverse in character. Thus, big data technologies are a gateway to a rich range of opportunities that help banks expand beyond their conventional sources of revenue.

Personalised Services

Despite all the buzz around relationship banking, traditional banks are not exactly known for personalized services. To thrive in the modern competitive world, banks need to make the analytics-oriented shift in perspective and craft customer experiences to impress. Banks can use data and analytics to add convenience to banking, making it more personalised for their customers and profitable for themselves. In combination with AI tech, big data can help banks identify customers who are dissatisfied with their services and can help remedy the situations in time. Incorporating customer’s preferences and issuing personalised responses enhance the service quality.

Banks can polish their service at an individual level by recommending economical solutions for handling transactions to customers and offering automation for recurring functions etc. Using big data for their advantage, banks quickly anticipate the needs of their customers and device solutions to cater to them in the most suitable ways, well in advance.

Optimising Consumer Journeys

Modern customers are well-informed decision-makers who make buying decisions and purchases whenever it’s convenient for them. Consequently, they expect seamless experiences across channels from their banks. Banks need to reassess how they understand and connect with customers. Data-driven insights can empower banks to understand each customer’s journey across channels. With digitisation of core banking processes, the footprint has increased and can be efficiently harnessed to derive insights into customer behaviour. Banks need to understand each customer’s journey, and for that, they need to analyse all of their customer data across all channels at the same time. Big data analytics efficiently showcases behaviour patterns that help banks turn leads into customers. Additionally, banks can determine the kind of customer behaviours associated with a high propensity to churn. Banks can proactively engage with customers through marketing efforts in such cases.

Identification of Opportunities for Upselling and Cross-selling

New age banks can expand their business by offering more than just essential banking services to their customers. Cross-selling can be a profitable proposition for growing companies when done right. Offering customers a product or service related to whatever they are already buying through cross-selling is desirable for customer experience. With big data, banks can promote different products and services to their customers based on their individual needs and interests. With big data put to correct use, banks can land new clients and enhance customer lifetime value by pedalling multiple product holdings to different customers. Even upselling banking services and products benefits banks.

Big data-backed cross-selling and upselling not only boost the sales revenue for banks but also help achieve customer service goals. Customers appreciate being informed about new products or services that might better meet their needs or about services that were not offered in the past, which creates value in the present.

From Brokers to Advisors

For most of the developed and developing countries, customers trust banks because of their diligence and the scale at which they function. With the widespread adoption of big data technologies, customers will likely trust banks with their sensitive data for the same reasons. As of present, most tech companies struggle with acquisitions of misuse of consumer data and the absence of transparency about data collection. Working with banks within the strict regulatory environment, it is likely that fewer issues about customer data use and collection with the surface.

However, for banks to become big data giants, they will have to re-evaluate how their clients trust them. Although banking customers have transactional trust in banks, advisory faith is a far-flung goal for most institutions in the country. Bank customers believe that putting their money in the bank account is the safest recluse, which they can access any time without any risk. However, not many customers turn to banks for their recommendations or counsel when it comes to financial decision making. Even B2B players view banks in the capacity of capital providers and financiers but never believe in them as counsellors whose interests align with their own. Banks will have to cultivate the advisory trust by acting as trusted advisors to its customers. The use of customer data to understand and develop appropriate digital business models will help banks change the customer’s perception of them. With big data and the right technology, banks will manage vast volumes of data and be the ‘big data giants’ they intend to be.

To Sum Up

Banks need to embrace big data before it’s too late. The incentive to embrace digitalisation and leverage data resources optimally varies based on regulations and consumer sophistication. If banks fail to keep up with the times in the use of big data, they will continue functioning as capital-intensive and low-return businesses. At the same time, tech companies collaborate with more aggressive traditional competitors to overtake attractive companies for themselves. As of today, only a limited number of traditional banks have turned data, analytics, and AI into their business goals, and still fewer seem to have integrated these capabilities into their core processes. Preliminary data integration leaves many attractive parts of banks’ business vulnerable.

Moving forward, banks will transform into data sources aiding customers in making all kinds of financial decisions. In a quest to ease customer experience, banks are progressing towards a future without any fees, i.e., they earn not from residual income from their customers’ transactions, but from outsourcing their data resources to other service providers. So much so that banks can quickly capitalize on their customer data given that they hone the public’s trust in the institution.

Finezza is one such lending management framework that helps banks and other financial institutions with data-backed risk analytical capabilities. The software itself excels at data extraction accuracy from fields like name, date of birth, and address, etc. easing the paper load off the shoulders of banking officers. It comes with a unique document recognition and data structuring module that can drastically reduce the end to end time used to populate the required fields of a document from 1-2 minutes down to 5-10 seconds, lending greater efficiency to banks. The software tool also aids intelligent debt collection thanks to big data.

Embedded Finance: How Can Banks and Lenders Utilise Customer Spending Data

Loan Origination System

Embedded finance is technically a package deal.

It combines a non-financial consumer experience with a suitable and usually complementary financial product or service. All in all, this combination aims to provide a superior customer experience. By 2027, global embedded finance market revenues are expected to exceed $183 billion.

Embedded finance brings three advantages in one go. First, it renders convenience to the consumer, opens new service delivery channels for the financial service providers, and finally, creates new revenue opportunities for the non-financial companies, serving as touch points.

Aside from the apparent benefits, embedded finance offers excellent opportunities to collect actionable customer data by making reporting, data collection, processing, and analysis easier for participants.

In this blog, we will discuss how fintech companies, banks and incumbent lenders can use customer spending data to stake their claims in the emerging market.

Vital Aspects of Customer Spending Data

Edge against competitors

A consumer’s spending data sheds light on his financial and behavioural priorities. When employed correctly, this information can be leveraged for fighting competition.

Studies have shown that companies that use consumer behavioural data for strategic decisions outperform their competitors by 85% in sales growth and by more than 25% in gross margin.

Overview of Granular Details

Consumer spending data tracks when, where, how much, and what a person or business entity spends money on.

It also encapsulates other details like how frequently they purchase things and whether or not they repurchase the same products or revisit the same seller. Such information has a critical bearing on product engagement strategies and marketing initiatives.

Basic customer expenditure data inherently records the following details:

Purchase amount/ money spent
Transaction date and time
Merchant name or ID
Payment method- credit purchase, prepaid or cash-on-delivery, EMI, etc.
Transaction currency and exchange rate
Details of customer loyalty programs, promo codes used, and coupons redeemed

What kind of personal details are included in spending data?

Based on what the platform/ site records, the spending data may also include the consumer’s personal details like name, mobile phone number, email id, age, gender, etc.

However, individuals voluntarily provide these details and are not captured automatically by the program.

Users must typically log into the Application Programming Interface (API) to access financing solutions via embedded finance.

How is Customer Spending Data Gathered?

Every time a transaction is made, a record of that transaction is generated. This record will contain all the above details regarding the purchase.

Spending information is also obtained using debit and credit card transaction details, details of ACH (Automated Clearing House) payments, Point Of Sales (POS) data (at the physical check-out counters), digital receipts, etc.

For e-commerce transactions, log-in and check-out processes are sources of customer spending data. Loyalty program codes, rewards, and promo codes also capture relevant customer data. Website cookies are another important data collection tool.

In most instances, the data collection is frequently erratic and delayed. Embedded finance technologies, on the other hand, provide businesses with real-time, raw data that algorithms can parse and use to detect behavioural patterns.

How can Banks and Lenders Leverage the Spending Data?

The spending data is used to observe, identify and predict consumer behaviour. This information aids in the customisation of product design, service delivery, and consumer engagement initiatives.

Here are some ways in which banks and lenders utilise spending data:

Marketing analysis

Customers’ spending habits accurately measure the success of your customer loyalty programmes and advertising campaigns. If buyers consistently choose a competitor despite your enticing exclusive customer retention programmes, you should reconsider your strategy and revamp the schemes.

Financial product development

Banks, fintech lenders, and non-financial enablers who own the embedded access points can use inputs from spending data analysis to develop new products and strengthening their product offerings.

Offer recommendations to customers

Machine learning algorithms can detect patterns in financial data and predict when the consumer will be paid, what products they will purchase, whether there will be any recurring payments such as bills or subscriptions, and so on. These programs can generate personalised product recommendations based on customer behaviour data.

Identify risky leads

This use case is particularly relevant to embedded lenders. Financial institutions use spending data to understand individual spending patterns, payment preferences, credit card usage frequency, and so on. Machine learning can identify non-linear relationships in transactional data, common to credit risks. As a result, lenders better understand their customers’ repayment behaviour and achieve greater control over risky lending.

Personalise customer service

Spending data allows a more in-depth understanding of a consumer’s needs and purchasing habits. Sellers can use this data to personalise discount offers, promotional campaigns, and customer engagement programmes to provide superior customer service to their clients.

Deepening customer relationships

An individual’s financial services requirements are linked to the lifecycle stages. Customers make major financial decisions corresponding to life events such as marriage, house purchase, retirement, etc. Banks can use data and other indicators to predict significant life events and connect with customers at the right time, and such positive experiences will increase customer loyalty.

Embedded Finance: The Future Outlook

Embedded finance is currently dominating the fintech segment at the moment. The service model is integrated into the financial ecosystem, and the market is gearing up for more competition. Embedded finance is becoming increasingly popular in the areas of payments, lending, card issuance, and deposits. In the future, we will see an increase in collaboration between incumbent financial institutions and fintech in the field of embedded finance.

Read how partnering with fintechs helps banks unlock sustainable growth.

The hordes of transactional and non-transactional data generated through embedded finance partnerships will give banks and lenders a competitive advantage.

However, data is not the only differentiator. Robust analytics aids in correctly reading data and providing accurate insights, and faulty analysis may generate unfavourable outcomes. As renowned British economist and Nobel laureate Ronald H Coase aptly articulated, “if you torture data long enough, it will confess to anything”.

Using Finezza’s Comprehensive Analytics Suite, you can avoid flawed analytics during lending decisions. Our analytics suite employs Machine Learning and Big Data Analytics to incorporate organisational learning. Users of Finezza can also create customised reports that they can access via our portal, real-time chatbots, or email.

Contact us today to learn more about our products.

Why Are Lenders Betting Big on Supply Chain Finance?

Supply Chain Financing

Micro, Small, and Medium Enterprises (MSMEs) are often considered the backbone of the Indian economy, and they account for approximately 30% of India’s GDP and employ close to 111 million individuals.

The COVID-19 pandemic posed many challenges across industries and disrupted supply chains. The current Russia-Ukraine war and periodic lockdowns in China have added to the woes of MSMEs, which also struggle to be a part of the traditional financial ecosystem.

Supply Chain Finance (SCF) offers a lifeline to this sector in these difficult times. In this blog post, let us discover how it can benefit the stakeholders and explore ways to improve its efficiency.

What Is Supply Chain Finance and How Does It Work?

SCF is a short-term working capital arrangement where an enterprise gets its supplier payments financed by an external financier. Supply Chain Finance involves three stakeholders: the supplier or the seller, the buyer and the bank or the financial institution (FI) that offers finance.

The business owners or suppliers sell their high-value invoices to a financial institution at a discounted rate to avail of short-term credit for meeting their working capital requirements. Buyers agree to approve their suppliers’ invoices for a lender to finance the operational capital requirements of a business.

SCF is available both in online and offline modes. Therefore, suppliers can raise invoices online on the dealers to avail credit instantaneously. The supplier and buyer benefit from this arrangement; the supplier gets faster access to funds owed to them, while the buyer gets more time to repay the dues.

On the due date, the buyer pays the financier the total price, and the FI gains from the difference between the discounted price paid to the seller and the total price received from the buyer.

How Supply Chain Finance Benefits All Stakeholders?

Supply Chain Finance benefits all three players of the ecosystem. The lenders are bullish about this mode of financing owing to the benefits it offers to all three participants paving the way for greater acceptance across the MSME sector.

1. Advantage To Suppliers

Here are some advantages Suppliers can avail of from SCF:

Suppliers receive immediate cash in hand against their unpaid invoices. Even though, at a discount, suppliers gain from instant liquidity.
SCF also results in reduced Days Payment Outstanding (DPO) for the supplier and eliminates the need for follow-ups for collecting payment.
They receive funds before the invoice due date and can utilise them for their working capital needs or pay other liabilities.
The supplier can use this process with other buyers to rotate funds more times within a year and increase their business turnover.
SCF offers them credit at lower rates compared to working capital loans, thereby reducing their finance costs.
Automation of systems helps them reduce operational costs.
The cash flow is streamlined, smooth and becomes certain.

2. Benefits to Buyers

Supply Chain Finance can help buyers in the following ways:

The buyer is relieved from the pressure of paying within 30 days as he gets an additional 30 days to make the payment.
A longer time for invoice payment gives the buyer more flexibility to use the available working capital till the payment due date.
SCF offers the benefit of reduced cost of goods purchased.
As payment is not an issue, suppliers are willing to provide a regular supply of goods, which ensures smooth operations.
It also fosters a stronger supplier relationship by providing a smooth working capital flow.

3. Helping the Lenders

Lenders can avail of a few benefits, such as:

Supply Chain Finance allows lenders to diversify their risk.
Lenders can utilise their underused funds to generate income through interest.
Their collaboration between sellers and buyers helps them increase their customer base and improves the opportunities for cross-selling products.

Why Are Lenders Bullish On Supply Chain Finance?

In light of adverse geo-political situations, businesses have been forced to look for innovative solutions to deal with rapidly changing market dynamics. Lenders have spotted a viable growth potential in the SCF model. It has helped organisations overcome their working capital woes while benefitting suppliers and assisting the FIs to diversify their risks and expand their operations.

The Supply Chain Finance sector is poised for a paradigm shift owing to digital adoption and technological advancements. Cash, as we understand, is the lifeline for any business and the working capital finance gap is a concern for most organisations. Automated cash inflows and outflows would help enterprises preempt and manage working capital gaps more efficiently.

Role of Technology in Supply Chain Finance

1. Leveraging AI and ML-enabled Fintech Solutions

Artificial intelligence (AI) and Machine Learning (ML) provide real-time projections on changes in payment patterns which helps businesses take timely action to avoid a cash flow crunch.

Fintechs are offering cost-effective finance to MSMEs by leveraging technology. Customised ML models and alternate data points for assessing creditworthiness are helping NBFCs, and banks gain better insights into repayment patterns and predict defaults. AI and ML algorithms offer dual benefits: indicate defaults and identify potential financing prospects.

2. Deploying LMS

Innovative solutions like a Loan Management System can help lenders manage multiple loan types and varying repayment frequencies and assist in non-performing asset (NPA) management.

Emerging technologies like blockchain can promote the spread of supply chain finance to cross-border trades. As multiple players are involved in such transactions, the validity of shipments becomes a cumbersome task. Blockchain can help do away with a central authority to help make the process seamless and increase transparency for all players.

As Supply Chain Finance emerges as an alternative financing model, the contribution of MSMEs to the economy will rise further and drive India’s inclusive growth story.

Few Opportunities In Supply Chain Finance

E-commerce can help lenders support multiple supply chains.
FIs can fund backwards and forward integration along the value chain by adopting an integrated approach.
Underserved market segments like consumer durables, commodities, electricals, FMCG, and agro-industries hold great potential to fund their supply chains.
Digital platforms and automation allow FIs and stakeholders better system integration and information sharing.
Implementing a digital SCF platform offers benefits like automation, risk mitigation, tracking, and monitoring the end-to-end supply chain.

In Conclusion

With Supply Chain financing, MSMEs can access working capital at a lower cost and reduce their dependence on informal channels. It stimulates the growth and development of the sector by providing uncomplicated access to finance.

Finezza’s intuitive product suite comprising Loan Origination Software and Loan Management Software focuses on facilitating faster and more efficient loan management to assist financial institutions and other lending ecosystem stakeholders in monitoring and managing credit applications, processing KYC documents more quickly, and streamlining the entire lending process.

Talk to us to learn more!

Rising Concerns Around Data Privacy with Growth of Digital Lending

Updates

The digital lending landscape has witnessed a tremendous boost, especially during the Covid, which has shifted borrowers online searching for viable loan products online.

Fifty-seven per cent of consumers prefer internet banking, according to the latest World Retail Banking Report. That’s nearly half the world’s commercially active population reaching out to online banking channels. So, of course, this has given rise to banks and NBFCs taking the online route for offering and processing loans.

However, every development has its downsides, and this one is no different. Increased digital lending penetration means more personal and sensitive financial data being transferred over the internet, which has raised valid concerns around data privacy amongst the stakeholders. However, 87% of consumers said data privacy is a human right, according to a study published by KPMG in 2020.

So, how are banks, NBFCs, and other lending institutions planning to safeguard their customers’ critical data and handle data privacy issues that creep up during day-to-day lending operations? Some of these concerns have forced institutions worldwide to consider data privacy — an indispensable aspect while giving out loans.

Digital Lending and Rising Concerns around Data Privacy

The lending space has seen a considerable inclination towards digital routes for sourcing credit. As a result, 55% of the buyers use online tools for all their credit needs. In fact, by 2022, smartphones are expected to meet six out of ten transactions for personal loans and seven out of ten transactions for other retail loans. This means roughly 40 – 60% of loan purchase transactions across loan types are influenced via digital channels.

The digital lending sector is expected to become the highest penetration sector by digital channels in India by 2023, with a growth rate of 48% and an increased valuation of $350 billion from an earlier valuation of $110 billion in 2019.

These big numbers are sure to boost the lending ecosystem and bring out rising concerns around data privacy.

According to a global economic crime survey, cybercrime has surged like never before, parallel to the digitisation boom. And cybercrime (including data breaches) is now the top recorded financial crime. In addition, cybercriminals have discovered new ways to attack and breach data as the world has gone digital.

Banks in India have been targeted by organised criminals and hackers regularly. It was demonstrated in a recent instance involving Canara Bank, in which a hacker attempted to stop some of the bank’s e-payments by putting a rogue page on the bank’s website.

Hence, as a solution, lending institutions need to partner with fintech to provide data privacy solutions. Data should be treated as ‘currency’, and they must adopt data-driven lending programs to predict borrowers’ lending patterns and credit behaviour.

Customer data and privacy are sacrosanct to ethical digital lending endeavours. Institutions that have been able to deploy effective data management practices focused on safeguarding their customers’ data have seen better customer loyalty.

This development is riding on the fact that consumers are now increasingly aware of their rights in and around their shared data across several platforms.

What Threatens Data Privacy in Digital Lending?

Digital lending has been under the scanner for various data privacy issues that may crop up, putting important, private information at risk. This renders data privacy an indispensable part of the whole lending ecosystem.

However, specific issues threaten the very possibility of implementing proper data privacy protocols at the grass-root level. Some of them are as follows:

1. Low importance

Data breaches are underrated when in fact, they are of high priority. But, unfortunately, they are neglected in budgets and do not enjoy as much support from the top management.

2. Lack of Awareness

Since institutions accord low priority to data privacy issues, people generally lack awareness about its critical aspects. Moreover, most firms fail to invest in proper training and understanding of the same amongst their employees.

3. Mobile Phones

Mobile phone transactions have shot up significantly, especially for people who prefer cashless banking. Unfortunately, this has given a breeding ground for hackers to execute different frauds, which lead to data breaches.

4. Social Media

One of the major platforms used by exploiters to steal customers’ private data is social media channels. As more and more people share their confidential information online today, data privacy is under serious threat.

Data Privacy Legislations

It would be apt to shed light on the fact that data acquisition is an integral part of lending operations, and it is impossible without the same. Therefore, the typical lifecycle of a loan will entail multiple steps, which will involve collecting, processing and analysing data.

This forms an essential checkpoint for the lending entities to determine borrowers’ creditworthiness and customise their offerings according to their loan needs.

Coming back to increasing consumer awareness, several countries are taking the initiative to enforce appropriate data privacy laws, including India.

The European GDPR rules, for instance, are the current gold standard in terms of personal data security policies and are backed by big tech giants like Apple and Google.

India’s Personal Data Protection (PDP) Bill 2019, has brought in its wake specific laws that have managed to transform the lending norms for banks, NBFCs and the various fintech companies that exist in the lending ecosystem.

Eight Keys to Stay Data Privacy Complaint

Since a lender has a fiduciary duty to its consumers, they must work with third parties who adhere to the highest data security standards to secure their customers’ complete data privacy.

All enterprises that want to succeed in this new data economy must adopt best-of-breed encryption (symmetric and asymmetric capable) standards like AES/PGP for both data in motion and at rest, advanced 2FA security models, frequent VAPT/data security audits, and vendor evaluations as baseline standards.

Here are certain compliances that lenders need to adhere to according to the rights drafted in the PDP Bill:

#1. Understand the Importance of Security

The importance of security and security systems needs to be excited significantly within the banking and other such institutions. Only when security is looked at as a plus for regulatory compliances can the mindset of relegating it as a cost will be shed.

#2. Invest in Emerging Technologies

Banking and lending institutions need to invest accordingly into technologies that can identify malfunctions and predict possibilities of frauds that can occur at various data points.

#3. Generating consumer awareness

It is one of the most crucial areas where the consumer must be made aware of the importance of not giving their banking credentials to anyone. They must notify the Cybersecurity cell as soon as possible if they notice anything suspect in their transactions or bank account.

#4. Informed Consent

Lenders need to have the explicit and informed consent of the customers before any of their data can be used for processing. Therefore, implied consent by the lenders is not a valid ground for the usage of personal data.

“Consent must be freely given, specific, informed and unambiguous for processing of personal data.” — EU GDPR

#5. Specific Purpose

The data collection should be limited to the extent that is required and necessary for processing. For example, the collection of data for reasons not known or not declared is not allowed.

Hence, lenders must agree to a privacy policy . It is a declaration or legal document that explains how a party collects, uses, discloses, and manages the data of a consumer or client.

Before data collection, the customer must consent to the privacy policy. A well-defined and thorough privacy policy is the greatest method to avoid regulators’ and customers’ concerns.

#6. Data Erasure

Once the purpose of the collected data is met, the data principal or the customer has the right to ask for the erasure of the data.

#7. Data Portability

Once the data has been used for the required purposes, data principals possess the right to receive a copy of their data in a structured and machine-readable format.

The above compliances will affect the following steps required in a typical lending situation.

#8. KYC Process

KYC or Know Your Customer process is the fundamental and preliminary step of any lending operation. Based on the documents required to execute the KYC guidelines, which are identity proof and address proof, KYC can be classified as a consent-based process.

Besides, the customers can request complete erasure of their data once the loan has been repaid. They can also request a copy of their data in the digital format because eKYC and VideoKYC are widely adopted across institutions.

Sticking Together with Regulations

To conclude, it would not be wrong to say that ‘Data serves as the oil running the wheel of current digital businesses’. Therefore, for lending institutions, safeguarding and protecting their customer’s data in a manner that their data privacy remains intact should be of utmost priority.

Emerging technologies such as blockchain, AI, ML and other risk management models will have a huge role to play in carving out data security strategies in this regard.

However, lending institutions need to stick to their guns and avoid data breaches from occurring at all times by adhering to data privacy laws and guidelines, which will only help them eliminate unnecessary legal fuss.

A futuristic approach that ensures an enhanced and safe lending ecosystem will go a long way in tackling data privacy concerns for all stakeholders.

5 Ways Digitisation Can Help Banks and Lenders Ensure Customer Data Safety

Updates

Before the advent of computers and technology, lenders and banks stored customer and transaction data in physical ledgers and registers. The physical ledgers, along with other confidential documents related to customers, were stored in safe vaults and lockers, which were guarded by armed physical security forces.

Since the advent of computers and subsequently, the internet, the financial and lending institutions’ financial landscape have changed drastically, redefining how customer data is stored. Physical ledgers and registers have become things of the past and have been replaced with powerful databases.

With the digital revolution and the advent of multi-channel banking software and apps, banks and lending institutions have seen a big surge in the number of customers and transactions. As the technologies grew, hackers have become smarter to maliciously attack the vulnerabilities in the financial infrastructure and gain access to confidential data. With the growing number of cybercrimes, banks are faced with a wide variety of challenges in maintaining the customer data safe.

Advanced Roles of Digitisation in Customer’s Data Safety

Below are some of the ways in which digitisation can help banks and lenders ensure customer data safety.

Know Your Customer and Their Business:

Know your customer (KYC) is an important step in any business. Especially concerning banking, it is very important to understand your customer better to enable additional safety to the customer data depending on the risk tolerance of the customer and the net worth.

Even though all customers are equally important for a financial institution, high net worth customers, who have a bigger relationship with the bank, deserve additional safety. A few years ago, KYC used a simple paper form, where the customer was expected to fill in details about their demographics, income details, business, etc. The problem with that approach is, it is easy to copy the data, especially where there are third party agents involved in the collection of KYC.

Today, most of the banks have done away with the paper mode of collecting KYC. With digitation in full swing in most of the banks, it is a good idea to use the services of experts in KYC, who provide services like digital vaults, authentication, and digital signature services.

The biggest advantage with digital vaults is that they use encryption algorithms like Advanced Encryption Standard (AES), RSA algorithm, etc. These digital safety vaults should be accessible only by authorised personnel within the banks after proper authentication. This way, the KYC details are fully safe from any unauthorised access.

Multi-Factor Authentication and Biometrics:

Authentication requires confirming the identity of the person before allowing any transaction with the bank. With the wide variety of banking channels like physical, telephonic, online, mobile, ATM, POS, etc., banks can no longer have a simple user id and password as the authentication technique.

Nowadays, most banks have implemented multi-factor authentication, which includes customer passwords as well as OTP authentication, which is valid only for a few minutes. Some banks and lenders have also started using biometrics as an additional authentication to ensure the customers’ identity.

Advanced Audit Trails and Employee Authorisation:

Banks also need to ensure that only relevant employees have access to the data. Nobody in the bank, including the IT support division, should have access to copy or edit the customer data directly in the database. Even read access should be provisioned only on a need basis through a proper approval mechanism.

The digital platforms or the software used for customer inquiry should be available only to people who support customer inquiries and calls. Even in that case, the digital platforms must have advanced audit trail features that tell data access history. Every bank must have an independent audit division that does periodical checks on the audit trails to ensure compliance.

Distributed Data to Minimise Database Hacks:

Database hacks cost a lot to the financial institutions as well as to their customers. The impact of database hacks increases when banks deal with customer data from the healthcare or financial industry.

In the current days, most banks are transitioning to cloud storage where the data is store in a remote location in a secure data centre.

Even though the data centres have a lot of physical and digital safety like firewalls enabled, the banks also need to take additional measures to store the data safely. Especially, the personally identifiable information of the customers should be encrypted, and the data should be stored in distributed data centres, so that even if there is any database hack, the data becomes unusable to the hackers.

Security by Design in the Internet of Things (IoT):

With the increase in internet-connected digital smart devices, there is a lot of benefit for the banks. Especially the field agents and officers who travel a lot can access customer data from anywhere using smart tabs and digital devices.

Though the advantages are many from the business point of view, this brings a lot of risks since the customer data is being accessed through local networks from outside safe premises. For the IoT to be fully leveraged by the banks, the overall architecture used by the IoT devices should have inbuilt security as part of the design.

The banking software architecture’s overall architecture should take into account all network security methodologies like Antivirus and Anti-Malware, Data Loss Prevention (DLP), Email Security, Firewalls, Endpoint Security, Mobile Security, etc.

Final Words

With the number of financial transaction channels and the volume of transactions increasing worldwide every day, the number of cybercrime cases has also increased proportionately. With the technology available at hand even for criminals, it becomes far more challenging for the banks and lending institutions to counter-attack the cyber threats. Any illegal access to customer data by hackers not only takes a big toll on the reputation of the banks but also creates irreversible damages to the customers.

Hence, it becomes crucial for the banks and financial institutions to invest enough to build a safe digital infrastructure. Above are some of the important aspects of digitisation, that can help the banks to create a digital fortress and a robust financial system to safeguard customer data.

Finezza provides an umbrella of tools and software for banks, NBFCs, and other lenders to accurately evaluate their clients and manage their financial transaction. Get in touch with us to know more about how we can help your business.

How Digital Footprint Data is Shaping the Future of Credit Scoring?

Lending Lifecycle Management

With changes in the way transactions are processed and payments are made, the system for accessing risks or loan underwriting must also change. Lending companies are undoubtedly a susceptible target of fraudsters, especially in the times of digital banking. Where traditional credit scoring fails miserably during digital lending, digital footprints can help make up for missing data.

Legacy Credit Scoring Model

All credit bureaus perform credit scoring for risk assessment. Credit scoring helps lenders figure out how the borrowers can pay back the loan in future. Legacy credit scoring models factor in income statements, spending habits, debt history, and card usage patterns. With credit scoring, credit bureaus aim to create a model that is for to predict how safe it is to lend to a particular borrower.

Here are some of the reasons why the legacy credit scoring model needs an upgrade:

Data from traditional credit bureaus can be falsified or stolen. For example, identity fraud and synthetic ID fraud are the biggest deterrents to traditional credit scoring models. Cybercriminals can easily take someone’s real ID, including someone’s name, social security number or bank account and use it in combination with fictional data to fulfil their plots. They often take leverage of details of individuals who have a spotless transaction history.
They leave no stone unturned in borrowing money from financial lending companies who lack security measures.
Advanced fraudsters go to the extent of testing data through their background check systems like paid services for credit checks online. Fraudsters usually build their application file using a stolen credit card and then access themselves using such services available online. They purposefully use means to score high on credit rating and apply for loans to ascertain that their repayment rate is higher than other loan applicants.
There have been instances in the past, where fraudsters even get access to the questions from background check companies, and craftily manufacture the betting user profiles to meet all the enlisted criteria for loan approval.

Legacy systems and their archaic processes limit banks and Fintechs. Most of the traditional credit scoring algorithms use logistic regression to churn scores and have the capacity to handle only about 30-50 variables at best, which keeps them from assessing comprehensive data for risk assessment.

What is Digital Footprint Data?

A digital footprint can be described as any collection of information that can indicate the same user as social media profiles, web cookies, or even specific patterns in online behaviour. When it comes to fraud prevention, factors like purchase history, browser used, and IP address also make useful components of the digital footprint data of an individual.

Limitations of Digital Footprint Data

Although the penetration of financial services has seen a surge in recent times, some still do not have access to them. For example, when it comes to developing countries of the world where a chunk of the population is still unbanked, there is an acute shortage of digital footprint data for loan companies to access. Even in developing nations, some people still can’t afford to pay banking fees, do not trust financial institutions, do not have adequate documentation and just use a friend’s or family member’s account for banking transactions.

How is Digital Footprint Data Shaping the Future of Credit Scoring?

1. Alternative Data for Credit Scoring

Digital footprint analysis is a process that uses information from individuals’ Internet usage patterns to understand who they are. As the adoption of digital mediums is increasing day in day out, this can be an invaluable source of useful data, even in countries where the percentage of unbanked citizens is high.

KYC is an excellent example of how a digital footprint can be accessed. The device used to fill out a KYC form can be useful for lenders. The equipment used to connect to the lending website presents info about whether an applicant is using private mode or an emulator. Through KYC form, lenders can also assess email profiles and check if it is from a suspicious domain or if it requires any additional verification during the signup process. Phone analysis allows lenders to check if applicants are signing up with a real phone number, regardless of whether it is from a fixed line or mobile and also if they are using that number for messaging services. KYC also facilitates IP analysis that verifies the origin of the connection.

Although using alternative data making up for digital footprint may seem extra, it can help ward fraudsters away. Newer credit score evaluation algorithms can easily multivariate and can mine, structure, weigh, and use rich data for futuristic credit scoring. Modern lending companies analyse various data sets to assess the creditworthiness of consumers applying for loans. Other alternative data sources include online presence, smartphone metadata, psychometric data, social media data, remittance history, utility bill payments, e-commerce merchant rating, etc.

2. Predict Credit Behaviour

Borrowers in emerging financial markets often have a negligible credit history. Digital footprint data can help lending institutions sanction loans to such individuals. Moreover, the use of advanced data helps Fintechs leverage a mix of historical data with new consumer data to predict credit behaviour aptly. With the use of data analytics, these diverse data sets can be used to derive relevant consumer insights.

Use of Artificial Intelligence (AI) and Machine Learning (ML) technologies has helped Fintechs shape a new wave of credit accessibility for consumers and small businesses alike. AI can help financial institutions ‘learn’ more about their consumers to make intelligent credit decisions. Combined with ML, AI can help screen the prospective borrowers by predicting their spending and saving behaviour accurately. Lenders benefit from lower NPAs, easy turnaround times for processing loans, and better ROIs.

3. Better Customer Experience

Lenders struggle to find a competitive edge in the markets with an increase in the number of players. Trying to deliver the best in class customer experience, Fintechs can leverage digital footprint data for personalisation and high-quality customer experience. Efficient data mining and analytics help loan origination systems in assessing the risk level that a prospect faces and can suggest ways to personalise a loan product to suit their needs in the best possible manner. Financial lending institutions can also devise collection strategies according to the customer’s credit risk level.

Key Takeaways

Fintechs are trying to build a robust credit scoring model that’s viable with their scaling operations. However, there is a universal distrust in the information they acquire through standard processes.

Finezza is a brilliantly designed analytics suite that incorporates various ways for credit scoring using digital footprint data. The SaaS platform facilitates data sourcing based on interactions and needs of different lending companies who decide to leverage its conveniences.

The Finezza ecosystem uses a winning mix of both traditional and alternative sources of data for efficient credit scoring. The platform conducts an in-depth analysis of underlying financial transactions of the applicant and supplies a practical index that reduces the NPAs for lenders.

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