Category: Banking

Welcome to our specialized banking category, an expertly curated space where we delve into the exciting world of digital transformation sweeping across the banking industry. Our insightful articles explore a multitude of facets that are actively shaping the future of banking, from the disruptive force of cutting-edge technologies to the shifting paradigms of customer expectations.

As the terrain of the sector undergoes continuous evolution, it becomes increasingly crucial for industry professionals, enthusiasts, and observers to keep abreast of the latest developments and trends. Our in-depth articles serve as a resource, providing valuable insights into how traditional banking institutions are navigating the transition to the digital age, and how emerging fintech companies are spearheading innovative solutions that are redefining the industry’s contours.

Our coverage encompasses a wide array of topics, from the convenience and the ubiquity of digital payments to the transformative impact of regulatory technology and the paramount importance of data security. This comprehensive exploration offers our readers a panoramic view of the digital banking ecosystem. Our overarching objective is to equip our readers with a nuanced understanding of the challenges that lie ahead, as well as the opportunities that digital transformation brings to the fore.

So, immerse yourself in our curated content, enrich your knowledge, spark meaningful conversations, and stay ahead of the curve in this digital banking era. Join us in exploring the captivating world of this sector where the only constant is change. Welcome to the future of banking, with us!

Making KYC Digital For Mutual Funds In India — Landmark SEBI Guidelines & The Way Forward

Posted on | by Signzian

The onboarding process for Asset Management Companies (AMCs) is among the most complex of all client-facing activities. Reams of documentation are exchanged between a client and the investment management firm. It is then distributed throughout the organization. Most of this requires approvals, signatures, and validations.

Digital onboarding requires finalizing legal agreements, Know Your Customer (KYC) and Anti Money Laundering (AML) activities. It also involves opening client accounts on multiple systems and transitioning incoming assets. Each of these activities engages multiple groups throughout the organization. Examples include client service, legal, compliance, and operations. Without well-defined and coordinated procedures, this could lead to errors. Ex: misplaced information, breakdowns in communication, and duplicated efforts are likely. The right-hand needs to know what the left hand is doing in order to properly manage all the hand-offs and moving parts.

Benefits of improving onboarding:-

  • Ability to generate fees sooner.
  • Increased potential to cross-sell, additional products, and services.
  • More referrals from clients due to a positive experience.
  • Reduced client turnover.
  • More efficient resource allocation.
  • Better views into process status.
  • Fewer mishandled communications and handoffs between the team.
  • Measurable efficiency through metrics.
  • Faster addition of new products and services.

Why Digital KYC? The Need For Digitization Of KYC In Mutual Funds

  • At present, investing in a mutual fund requires a second round of KYC. This is also true even for customers who have completed KYC in their bank accounts. The procedure involves the submission of identification and address proofs along with photographs. The distributor or adviser must physically meet the customer to conduct ‘in-person verification’ for him/her. This requirement greatly hampers the growth of mutual funds online.
  • It also affects access to mutual fund investments for those in remote areas. In 2019, the Nilekani committee proposed that there should be a simple KYC procedure for opening a mutual fund account funded from a KYC-verified bank account. However, inflows into such a folio and redemptions to it must be restricted to this account.

This leads to the digitization of KYC. Among the many advantages of getting paperless KYC done, the following benefits are most important:

  • Personal Details are Secure: All information is stored and transmitted on the website with a special configuration. Whether it is your Account Information, Demographic Data, Biometric Data, etc. The KRA, Fund House, or AMC’s Portal is maintained with the highest level of Security. It reduces illegal activities of money laundering, loan scams, identity theft, and fraud.
  • You are the Boss: The option to invest will always be yours. The digital KYC mechanism is completely dependent on your decision. Not only that, you have the choice of providing access to your details to whomsoever you want. In some cases, if you change your mind. You may not want to invest in Mutual Funds. Whereas, if you opt for offline KYC. It is possible that your self-attested documents end up with unauthorized parties. This risk gets reduced to a large extent by taking the online KYC mode.
  • Instant Process: No Human element is involved that means no Red Tape is involved. The efficiency in the digital process ensures no delays. Comparatively, the offline process would take at least a few days.
  • Transparency: Incidents of the KYC documents in illegal and illegitimate persons occurred commonly. Opting for Online KYC, you can avoid such an event. The websites store the data in encrypted servers. It makes the possibility of a breach highly unlikely. Besides, the trespasser or the source of the breach can be traced in online transactions. They can be brought to legal authority with proof.
  • No Hidden Costs: Some Mutual Funds agents may charge extra amount as KYC Registration fees. And investors need to pay to avoid the hassle of taking time off from work and visiting the Government Agency in person. With eKYC, you do not need to pay in addition to the investment amount.
  • Compliance: Your data gets validated using the latest technologies. This increases the overall security of the system. It also ensures that the digitally transferred document is legally valid.

The Road To Digitization Of KYC

As per regulatory developments from January 1, 2011, KYC is mandatory for investors wanting to transact in Mutual Funds. This is regardless of the transaction amount. It implies that you will not be able to process any fresh MF purchases post January 1, 2011. This is true except when you are MF KYC compliant as per CDSL Ventures Limited (CVL) norms.

This implies that you can always ask your broker to provide you forms for submission to your KYC. Since there are no charges for mutual funds they may not be useful. As such, it is better you also understand you can get your KYC done. Follow these steps:

1. Get the Form

The KYC application form can be availed from the investor service centers for the particular Fund, CAMS or at any specified ‘Points of Service’ (POS) of CDSL Ventures Ltd. You can also download it from your broker, advisor or AMC.

2. Documents

The following lists the set of documents which are required for submission with the KYC application form:

1. A recent passport size photograph

2. PAN card copy

3. Address proof (Recent bank statement will work but if you have to get your bank statement in the email you need to visit your bank branch to get an original one.)

The document submission can be done at the CAMS Online office in your city. Ensure you carry the originals along with a photocopy of the documents because at times they might need to verify with the originals.

3. Verification

Once the KYC application form and supporting documents are verified, the investors will receive a letter authenticating their KYC compliance. They normally give you the letter in a few hours to a max of 24 hours for this identity verification api .

You can verify your KYC status online. You should verify on the day of form submission that your status is processing. Once it is done, your status should change to VERIFIED.

Actually KYC need not be done at your broker’s end. But some online systems do not accept the order. This can happen if they don’t have the data in their own system and so it is better to get that done as well.

KRA and K-IPV In KYC Collection

SEBI had initiated the usage of uniform KYC by all SEBI registered intermediaries (RIs). This was done to bring uniformity in the KYC requirements for the securities markets. In this regard, SEBI had issued the SEBI KYC Registration Agency (KRA), Regulations, 2011.

KRA is the authority for the centralization of all KYC records and details in the securities market. The client who wishes to open an account with a broker shall submit the KYC details. They can be submitted through the KYC Registration form with supporting documents. The Intermediary is responsible for conducting the initial KYC. The RI should also upload the details to the KRA system. The KYC details are accessible to all SEBI RIs for the same client. So once the client has undergone KYC with an RI, it is not necessary to repeat the same process again with other RIs.

It is compulsory for each client to be registered with any one of the various KRA registered intermediaries. This should be done before availing the benefits of any intermediary. Such benefits include Stock Broker, Mutual Fund Companies, Depository Participant, Portfolio Management Services (PMS) etc.

In-Person Verification (IPV) is part of the process of doing KRA-KYC registration of clients. KRA compliant clients are not required to undergo this process.

Importance Of IPV

The Prevention of Money Laundering Act, 2002 (PMLA), came into effect from 1 July 2005. The Act enforces that no one could use investment tools to hide their illegal wealth. Soon after, SEBI mandated that all intermediaries should adopt the KYC policy. It was also necessary to plan and install certain policies. The policies should follow vis-a-vis the guidelines on anti-money laundering measures.

Since 1 January 2011, KYC compliance has been made mandatory for all investors. This is irrespective of the amount invested and includes the following transactions:

a. New / Additional Purchases

b. Switching Transactions

c. First-time Registrations for SIP/ STP/ Flex STP/ FlexIndex/ DTP

d. Any SIP/STP/trigger-related products which were introduced after the enactment of the act

e-KYC (Know Your Customer) is a value-added feature that is offered by many financial institutions. E-kyc is useful for making the application process convenient. Investors can access it and upload the necessary documents. It can be done from the comfort of their home or office. As previously discussed, this is applicable to only SEBI-approved KRAs. For ex: CVL and CAMS can complete the e-KYC process. This means that Digital KYC can be used for IPV as well.

EKYC — The Miracle Turned Myth

To remove the repetitive submission of documents, SEBI launched the concept of common KYC in 2011. With this move, the first intermediary processes the KYC-related information and sends them to the KYC Registration Agency (KRA). Once your account is created, any other intermediary can make use of the same details in the future for new accounts.

Why eKYC?

The concept of common KYC smoothened things for retail investors, However, it was still a time-consuming process (8–10 days). It also included the problem of in-person verification. This also increased the cost of servicing small investors while preventing immediate on-boarding of new customers.

SEBI launched eKYC in order to make the procedure more investor-friendly. It enabled customers to verify their identity and upload documents digitally. To get started, you only needed to quote your Aadhaar number, PAN number, e-mail id, and mobile number. Once you type in the details, you will receive a one-time password (OTP) in your Aadhaar-registered mobile number. After entering the OTP, the eKYC process would be completed and you could start investing in mutual funds within minutes.

While Aadhaar based eKYC had been introduced as a means for onboarding, there were a lot of discrepancies. This was especially after the Supreme court judgement on the use of Aadhaar based eKYC. It was later reintroduced. This had left a state of confusion and many AMCs continued traditional methods of KYC collection for onboarding. Physical KYCs are more time-consuming. The distributor has to submit the documents to KYC Registration Agencies or KRAs. The KRA nodal agencies have to manually fill in the data in their systems from the applications. If the handwriting is illegible, capturing the KYC data could lead to errors. This would delay the process further.

The SEBI Way Of Digital KYC

In a recent move on April 24, 2020, the Securities & Exchange Board Of India (SEBI) has issued the latest guidelines on the digitization of the KYC process. Some of the highlights are mentioned below:

1. Know Your Customer (KYC) and Customer Due Diligence (CDD) policies form a part of KYC. They are the foundations of an effective Anti-Money Laundering process. The KYC process requires every SEBI registered intermediary (also known as ‘RI’) to collect and verify the Proof of Identity (PoI) and Proof of Address (PoA) from the investor.

2. The provisions as laid down under the Prevention of Money-Laundering Act, 2002, Prevention of Money-Laundering (Maintenance of Records) Rules, 2005, SEBI Master Circular on Anti Money Laundering (AML) dated October 15, 2019 and relevant KYC / AML circulars issued from time to time shall continue to remain applicable. Further, the SEBI registered intermediary will continue to ensure to obtain the express consent of the investor. This should be done before undertaking online KYC.

3. SEBI, from time to time has issued various circulars to simplify the process of KYC by investors / RIs. Constant technology evolution has led to multiple innovative platforms being created. These allow investors to complete the KYC process online. SEBI held discussions with various market participants and based on their feedback, technology like Aadhar-based e-Sign service which can facilitate online KYC will now be used. This is done with a view to allow ease of doing business in the securities market.

4. New regulations allow Investor’s KYC to be completed through an online / App-based KYC. There is also provision for in-person verification through video, online submission of Officially Valid Document (OVD) / other documents under eSign. It allows the introduction of VideoKYC, which was also allowed by RBI for the banking sector earlier this year. (Click here< to read more about RBI Guidelines for VideoKYC)

5. SEBI registered intermediary may implement their own Application (App) for undertaking online KYC of investors. The App shall facilitate taking photographs, scanning, acceptance of OVD through Digilocker, video capturing in a live environment, usage of the App only by authorized persons of the RI.

6. The guidelines also allow RIs to undertake the VIPV(Video In-Person Verification) of an individual investor through their App. This is done to ease investor onboarding.

Digital KYC For The New Era

Signzy has developed an AI-based electronic KYC solution called RealKYC. It consists of a host of microservices that provide the following benefits to AMCs

  • Reduction of TAT: During investor onboarding, the traditional method of KYC collection involves the submission of a lot of documents and processing that is done by several departments and their officers. This can be a time-consuming process but with VideoKYC, the entire process is automated and can be done in a matter of minutes in real-time.
  • Lower Operational Costs: The onboarding process for a new investor can require several checkpoints that are cost-effective. There is significant manpower involved as well which also raises the cost of onboarding. All these factors can be automated with RealKYC, thereby reducing operational expenses.
  • Remote Onboarding: With RealKYC, there is no need for investors/entities to pay multiple visits to the physical branch for the processing of KYC. They can simply visit the website and submit all their documents as well as get the verification done, online.

Signzy’s VideoKYC solution offers a simple, secure KYC collection process that is 100% compliant with the latest SEBI Guidelines. The benefits include:

  • Compatibility With Most User Devices: This solution has matured over dialects, browsers and low-internet scenarios. This means that most users can undergo VideoKYC without any technical pain points.
  • Improved BackOps; Our Patented AI reduces 90% Backops effort, making onboarding of investors a smooth process.

Conclusion

KYC or Know Your Customer is a compulsory requirement for those wishing to invest in Mutual Funds. It is mandatorily needed by the Market Regulator SEBI (Securities and Exchange Board of India). This identification process needs to be undertaken only once. KYC was introduced to avoid fraudulent activities. eKYC for Mutual Fund was launched for the ease of investors.Digitization of KYC merely changes the mode of KYC collection and not the process.

About Signzy

Signzy is a market-leading platform redefining the speed, accuracy, and experience of how financial institutions are onboarding customers and businesses – using the digital medium. The company’s award-winning no-code GO platform delivers seamless, end-to-end, and multi-channel onboarding journeys while offering customizable workflows. In addition, it gives these players access to an aggregated marketplace of 240+ bespoke APIs that can be easily added to any workflow with simple widgets.

Signzy is enabling ten million+ end customer and business onboarding every month at a success rate of 99% while reducing the speed to market from 6 months to 3-4 weeks. It works with over 240+ FIs globally, including the 4 largest banks in India, a Top 3 acquiring Bank in the US, and has a robust global partnership with Mastercard and Microsoft. The company’s product team is based out of Bengaluru and has a strong presence in Mumbai, New York, and Dubai.

Visit www.signzy.com for more information about us.

You can reach out to our team at reachout@signzy.com

Written By:

Signzy

Written by an insightful Signzian intent on learning and sharing knowledge.

 

Collaboration is the Key for Banks and Fintech Startups

Posted on | by Signzian

The banking ecosystem is in a continuous state of disruption. Traditional banks are trying to navigate the reality with legacy systems and hoping to switch to digital banking. Banking institutions are facing pressure due to rising customer demands for better customer experience.

For so long, banks and fintech companies stood on either side, competing against each other. However, they are fast learning that collaboration might be the key to their success. A joint venture between banking and fintech is the path to long-term growth, increased revenue, and satisfied customers.

Collaboration is the Key for Banks and Fintech Startups

Challenges for Banks on the Offline Road and the need for digital banking

The banking sector has traditionally relied on paper-based processes. Here are a few challenges banks face due to lack of innovation and digital banking:

  • Improving customer loyalty — Without a tech-enabled experience, it is challenging to provide convenience to the customer. When banks fail to innovate, they compromise with the services they offer. This, in turn, negatively impacts the loyalty of the customer.
  • Resource optimization — Without efficiency in operations, it is difficult to optimize resource usage. Both time and money are assets to any financial organization and a lack of technology hampers a bank’s ability to optimize its resources.
  • Personalization — Without data and analytics, personalization is hard to achieve. Therefore, banks face the constant challenge of learning about their customers’ varied interests and behavior when they don’t leverage technology.
  • Transparency — Trust and transparency get lost under piles of paper forms and applications. Therefore, banks that don’t opt for digital banking, fail to achieve process transparency and compromise with both employees and customers on the trust front.
  • Omni-channel — When customers want to make fewer visits to the branch, it is critical that their interactions with the bank be seamless across other channels. Digital banking can create an omnichannel experience through social media, website and mobile app platforms.

These challenges are not recent events, but banks have been facing them since forever. There was no way to resolve these issues, except by taking the help of fin-tech companies.

Why Legacy Financial Institutions are Under Urgent Pressure

The World Fintech Report 2018 by Capgemini outlines the following reasons why even the biggest banks face pressure today:

  • New business models — New trends such as Peer-to-Peer payments and lending, social network scoring solutions, and crowdsourced solutions are pushing legacy financial institutions to innovate.
  • Speed and efficiency — As customers demand better experiences, banks are forced to rethink their processes. Fintech startups are making digital banking accessible and convenient. Banks are expected to follow suit. Real-time updates, proactive notifications, alerts, and agile innovation are a part of the enhanced customer experience.
  • Transparency — That is something banks cannot achieve with traditional systems. Digital banking is needed to bring in transparency into the various processes. Fintech firms are leading the way by showing cost upfront and offering services at lower costs.
  • Personalization — Digital banking is better positioned to offer a personalized experience to their customers by leveraging data and analytics. If banks want to retain customers, they will have to level up their personalization.
  • Operational efficiency — Streamlines delivery and product development provide a significant competitive advantage. With a digitally-enabled solution, fintech firms are improving operational efficiency and pushing legacy organizations to innovate and reinvent.

Opportunities for Banks and Fintech Companies

According to a survey by PwC [1], 82 percent of insurers, asset managers, and banks plan to increase the number of collaborations they have with fintech startups over the next three to five years. And we see various ongoing acceptance from banks to collaborate with new innovation offered by fintech startup players.

Taking example from our own journey, presenting here case in point, Signzy, a platform that helps financial institutions:

  • Onboard customers through a seamless process that reduces hassle and friction.
  • Scale faster with an AI and ML-based regulatory engine.
  • Reduce costs.
  • Cut turnaround time.
  • Use advanced cryptography to create robust security and data protection infrastructure.
  • Leverage a range of white-labeled solutions to drive faster digital transformation.

Signzy is trusted by several large banking corporations such as ICICI Bank, SBI, Aditya Birla Financial Services, Mahindra Finance, Edelweiss, and so on.

Here are a few challenges Signzy’s fintech solutions help banks solve:

  • KYC — Banks need their customers to fill out KYC forms with their details. Traditionally, the process used to be paper-based. This meant that any mistake in one form would compel customers to start all over again. Signzy enables bank-grade digital KYC in real-time. An API matches the biometrics of the customer, checks the data in government records, and warns the user of potential document forgery as the customer fills in details.
  • Background check — Traditionally, the bank staff usually gather all identity documents from each customer and manually does a background check of the customer information. Signzy offers a simple and digital way to accomplish this. Algorithmic Risk Intelligence allows banks to do a holistic background check, discovering any court cases and legal lists, fetching anti-money laundering related data, checking the UN CFT List and NIA Most Wanted list.
  • Contracts — Signzy is replacing physical contracts with digital ones. These come with video and voice verification, blockchain implementation, biometrics, and high performance. Smart contracts are the way to go.
  • SME Onboarding — Merchant onboarding is a seamless task with Signzy’s offering that helps clients cut down onboarding time from 2 weeks to a few hours. Features include a mobile link with in-built regulatory rules, real-time document verification, Aadhar-backed contract signing, and AML background check.
  • Transaction Banking with Corporates — Signzy’s offerings can help banks automate complex regulatory procedures with AI and robotics to significantly reduce TAT and enhance the customer experience. Comprehensive risk and regulatory checks can help banks mitigate risk in dealing with large enterprises on both liability and asset products.
  • Insurance — The insurance process can be simplified and digitized with Signzy’s individual onboarding system. It simplifies the onboarding journey using advanced biometrics and fraud detection capabilities reducing risk and enhancing user experience.

There’s an entire list of solutions that Signzy offers to bank institutions to catalyze their digital transformation journey.

Roadblocks in Banking and Fintech Collaboration

Understanding the opportunity might not be equal to taking action for banks and fintech start-ups. Between varying cultures, different infrastructures, and ever-changing compliances, a collaboration between banking institutions and fintech companies is far from simple. Many opportunities and proposed deals get derailed as a result.

Here are top three things both sides must consider before finalizing a proposed partnership:

  • Consider any cultural gap — Make sure that the cultural match is not too challenging. There must be a willingness to adapt by both sides in a partnership.
  • Understand challenges — Collaboration with fintech startups can help financial institutions alleviate the major challenges they face.
  • Leverage data and innovation — The massive data that financial institutions have by their side is by far their most underused and critical asset. Fintech startups should leverage this data and innovate around it.

The success of collaboration rests with the organizations that can understand each other’s pain points and strengths, and work to improve the customer experience while reducing operational costs.

However, few fintech startups can offer the level of personalization, contextuality, speed, and seamless delivery to help financial institutions achieve digital transformation.

Signzy is capable of doing that. Want to collaborate with a forward-thinking fintech startup that can help you leverage digital processes?

About Signzy

Signzy is a market-leading platform redefining the speed, accuracy, and experience of how financial institutions are onboarding customers and businesses – using the digital medium. The company’s award-winning no-code GO platform delivers seamless, end-to-end, and multi-channel onboarding journeys while offering customizable workflows. In addition, it gives these players access to an aggregated marketplace of 240+ bespoke APIs that can be easily added to any workflow with simple widgets.

Signzy is enabling ten million+ end customer and business onboarding every month at a success rate of 99% while reducing the speed to market from 6 months to 3-4 weeks. It works with over 240+ FIs globally, including the 4 largest banks in India, a Top 3 acquiring Bank in the US, and has a robust global partnership with Mastercard and Microsoft. The company’s product team is based out of Bengaluru and has a strong presence in Mumbai, New York, and Dubai.

Visit www.signzy.com for more information about us.

You can reach out to our team at reachout@signzy.com

Written By:

Moni Gupta

 

AI-Based Digital Onboarding Transforming Banking Organizations

Posted on | by Signzian

Customers are increasingly demanding better digital banking experiences to match the integrated experiences they receive in other industries. Financial institutions continuously find themselves under pressure to deliver a mobile-first, customer-centric digital experience to customers throughout their journey starting right from account opening. Artificial Intelligence has the potential to drive digital transformation in banking and redefine the way financial institutions interact with customers during the digital customer onboarding process.

The onboarding system is generally the first experience a customer has with any banking institution and that in itself sets the tone of the ongoing experience the bank can deliver. The client onboarding process is also critical by the fact that it gathers Know Your Customer Data for continuing maintenance and management of the account.

AI in banking has become a priority, and bankers have identified two areas worthy of digital transformation in banking – KYC and digital onboarding.

 

How Can Banks Offer a Seamless Onboarding Experience?

There are four critical areas in which banks can focus to revolutionize the onboarding experience they offer:

  1. Digitize processes, indeed

     Often financial institutions fail to understand that digital is more than online and mobile. Their customers demand sophisticated interfaces, but banks continue to work through manual and paper-based workflows. This way, bankers are rendered unable to answer customer queries about process status and task completed. Enabling a single point of truth of each customer and eliminating manual, disconnected processes will empower bankers to quickly onboard customers and collect and manage their information seamlessly. AI in banking will also increase the quality of data and help minimize errors.

  2. Allow customers to bank anytime, anywhere

     Digital transformation in banking will help customers to carry out functions 24×7, from anywhere using their preferred device or channel. In turn, financial institutions will be able to market products across multiple channels, improving sales and customer retention. By introducing an Omni channel experience through responsive web design, banks will be able to offer frictionless user experiences allowing customers to undertake the digital onboarding process on one platform and finish it on a different one.

  3. Collect data once

     Customers often face frustration when they have to provide the same information over and over again for different banking tasks. Digital transformation in banking can change that. Banks have started experimenting with end-to-end interfaces that allow customer details to be entered once and reflect everywhere else. This cuts down a lot of hassle for both customers and banking officials. In conjunction with AI in banking, institutions are also considering the applications of blockchain to consolidate data across a financial organization.

  4. Personalize experiences

     Predictive analytics can be leveraged to learn more about customer behavior- where customers spend their time, where they abandon sessions, and so on. With this data, banking institutions can personalize experiences for customers and expedite the digital onboarding process.

Banking organizations are ripe with data ingested by intelligent capture only waiting to introduce processing intelligence.

Digitize the Onboarding System & Opt for Online ID Verification

AI is helping banks set up a seamless digital onboarding experience. Consider this: when a customer opens a new bank account or applies for a loan, they have to provide a number of documents and ids to their banks such as employment proof, identity proof, and proof of address.

Additionally, the bank staff needs to physically scan each document to verify specific clauses, values, and statements in their process. Then, bankers need to verify particular intentions or assumptions with the customer before deciding each customer’s creditworthiness for a loan or a product.

These manual activities are tiresome and can very well induce error anywhere in the end-to-end process.

With intelligent capture and online id verification, this can be done with a few clicks on a smartphone. Thus, saving bankers and customers a lot of time and effort.

Since customer satisfaction is a crucial differentiator for banking institutions today, automating and digitizing onboarding process can prove to be a game-changing strategy. In other words, the client onboarding process can determine customer experience, loyalty, referrals, sales, and profitability.

Hence, Digital transformation in banking will help financial institutions adapt to changing regulations quickly. With manual paperwork, turning a few regulations every month can seem a daunting task, while digital processes make the process easier.

How Signzy is Innovating in the Banking Space

Trusted by ICICI Bank, SBI, Birla Sun Life Mutual Fund, Edelweiss, MasterCard, PayU, HDFC Bank, and many other financial institutions, Signzy is a classic example of a tech-enabled solution disrupting the banking space.

Through Blockchain and AI solutions in banking and financial services, Signzy is reducing human interaction with customers at various levels, saving it only for the critical decision making aspects of banking.

Here are a few features Signzy offers across its product line:

  • Digital real-time KYC
  • Digital signature for KYC
  • Biometric signatures
  • Algorithmic Risk Intelligence to provide a satisfactory background check
  • Digital contracts

Signzy can help financial institutions decrease operational expenditure by 75 percent.

Case in Point: How Signzy led digital transformation in banking for its client

The existing process consisted of the following steps:

  • The customer manually fills in the application form.
  • The bank sales associate collects the forms and KYC documents without verifying.
  • The branch manager screens all documents to make sure everything is in place.
  • The central ops do a risk check on the documents collected.

If the documents have anything amiss, they go back a phase and the process restarts.

Here are the challenges the baking firm faced because of their manual processes:

  • Errors in filling out forms.
  • No real-time verification of the information submitted by the customer.
  • Missing documents or details.
  • Time-consuming process.
  • Manual documentation that’s hard to maintain.

The solution to this firm’s woes was a digitized platform to disrupt their inefficient process such as Signzy.

The benefits Signzy realized for this institution:

  • As customers filled out digital forms and submitted to bank associate, the information could be checked in real-time.
  • The verification process was automated as a result.
  • The auto-populating of form quickens the process.
  • Reduction in human typing errors by data extraction.
  • Company verification from government records.
  • The user experience was transformed into a smoother one.
  • The bank ops could now focus on key risk cases and undertake high-value tasks
  • Since the bank saw a dramatic reduction in their operational costs, their revenues surged.

Signzy can bring out massive measurable benefits for their customers through digital transformation in banking.

Signzy realized the following metrics for its client:

  1. 80% cost reduction in customer onboarding process.
  2. Reduction in the TAT from 3 days to 30 minutes- a huge time saving and a differentiator in customer experience.
  3. Three times more efficient sales.

As of the end of 2018, Signzy supports 51 customer accounts and answers half-a-million API calls each month. Signzy digitally transforms businesses focused on financial services.

About Signzy

Signzy is a market-leading platform redefining the speed, accuracy, and experience of how financial institutions are onboarding customers and businesses – using the digital medium. The company’s award-winning no-code GO platform delivers seamless, end-to-end, and multi-channel onboarding journeys while offering customizable workflows. In addition, it gives these players access to an aggregated marketplace of 240+ bespoke APIs that can be easily added to any workflow with simple widgets.

Signzy is enabling ten million+ end customer and business onboarding every month at a success rate of 99% while reducing the speed to market from 6 months to 3-4 weeks. It works with over 240+ FIs globally, including the 4 largest banks in India, a Top 3 acquiring Bank in the US, and has a robust global partnership with Mastercard and Microsoft. The company’s product team is based out of Bengaluru and has a strong presence in Mumbai, New York, and Dubai.

Visit www.signzy.com for more information about us.

You can reach out to our team at reachout@signzy.com

Written By:

Ankit Ratan, CEO-Signzy

 

Survey of facial feature descriptors

Posted on | by Signzian

Face recognition technology has always been a concept that lived in fictional worlds, whether it was a tool to solve a crime or open doors. Today, our technology in this field has developed significantly as we are seeing it become more common in our everyday lives. In the mission of building a truly digital trust system, we at Signzy use Facial recognition technology to identify and authenticate individuals. The technology is able to perform this task in three steps: detecting the face, extracting features from the target, and finally matching and verifying. As a visual search engine tool, this technology is able to identify key factors within the given image of the face.

To pioneer our facial recognition technology, we wanted an edge over the current deep learning-based facial recognition models. Our idea was to embed human crafted knowledge into state of art CNN architectures to improve their accuracy. For that, we needed to do an extensive survey of the best facial feature descriptors. In this blog, we have shared a part of our research that describes some of the features.

Local binary patterns

LBP looks at points surrounding a central point and tests whether the surrounding points are greater than or less than the central point (i.e., gives a binary result). This is one of the basic and simple feature descriptors.

Gabor wavelets

They are linear filters used for texture analysis, which means that it basically analyses whether there are any specific frequency content in the image in specific directions in a localized region around the point or region of analysis.

 

 

Gabor jet similarities

These are the collection of the (complex-valued) responses of all Gabor wavelets of the family at a certain point in the image. The Gabor jet is a local texture descriptor, that can be used for various applications. One of these applications is to locate the texture in a given image. E.g., one might locate the position of the eye by scanning over the whole image. At each position in the image, the similarity between the reference Gabor jet and the Gabor jet at this location is computed using a bob.ip.gabor.Similarity.

Local phase quantisation

The local phase quantization (LPQ) method is based on the blur invariance property of the Fourier phase spectrum. It uses the local phase information extracted using the 2-D DFT or, more precisely, a short-term Fourier transform (STFT) computed over a rectangular M-by-M neighborhood at each pixel position x of the image f(x) defined by:

where Wu is the basis vector of the 2-D Discrete Fourier Transforms (DFT) at frequency u, and fx is another vector containing all M2 image samples from Nx.

Difference of Gaussians

It is a feature enhancement algorithm that involves the subtraction of one blurred version of an original image from another, less blurred version of the original. In the simple case of grayscale images, the blurred images are obtained by convolving the original grayscale images with Gaussian kernels having differing standard deviations. Blurring an image using a Gaussian kernel suppresses only high-frequency spatial information. Subtracting one image from the other preserves spatial information that lies between the range of frequencies that are preserved in the two blurred images. Thus, the difference of Gaussians is a band-pass filter that discards all but a handful of spatial frequencies that are present in the original grayscale image. Below are few examples with varying sigma ( standard deviation ) of the Gaussian kernel with detected blobs.

 

Histogram of gradients

The technique counts occurrences of gradient orientation in localized portions of an image. The idea behind HOG is that local object appearance and shape within an image can be described by the distribution of intensity gradients or edge directions. The image is divided into small connected regions called cells, and for the pixels within each cell, a histogram of gradient directions is compiled. The descriptor is the concatenation of these histograms.

 

FFT

Fourier Transform is used to analyze the frequency characteristics of various filters. For images, 2D Discrete Fourier Transform (DFT) is used to find the frequency domain. For a sinusoidal signal,

we can say f is the frequency of signal, and if its frequency domain is taken, we can see a spike at f. If signal is sampled to form a discrete signal, we get the same frequency domain, but is periodic in the range

or

( or for N-point DFT ).

You can consider an image as a signal which is sampled in two directions. So taking Fourier transforms in both X and Y directions gives you the frequency representation of the image.

Blob features

These methods are aimed at detecting regions in a digital image that differ in properties, such as brightness or color, compared to surrounding regions. Informally, a blob is a region of an image in which some properties are constant or approximately constant; all the points in a blob can be considered in some sense to be similar to each other.

CenSurE features

This feature detector is a scale-invariant center-surround detector (CENSURE) that claims to outperform other detectors and gives results in real-time.

ORB features

This is a very fast binary descriptor based on BRIEF, which is rotation invariant and resistant to noise.

Dlib — 68 facial key points

This is one of the most widely used facial feature descriptors. The facial landmark detector included in the dlib library is an implementation of the One Millisecond Face Alignment with an Ensemble of Regression Trees paper by Kazemi and Sullivan (2014). This method starts by using:

  1. A training set of labeled facial landmarks on an image. These images are manually labeled, specifying specific (x, y)-coordinates of regions surrounding each facial structure.
  2. Priors, of more specifically, the probability of distance between pairs of input pixels.

Given this training data, an ensemble of regression trees is trained to estimate the facial landmark positions directly from the pixel intensities themselves (i.e., no “feature extraction” is taking place). The end result is a facial landmark detector that can be used to detect facial landmarks in real-time with high-quality predictions.

Code: https://www.pyimagesearch.com/2017/04/17/real-time-facial-landmark-detection-opencv-python-dlib/

Conclusion

Thus in this blog, we compile different facial features along with its code snippet. Different algorithms explain different facial features. The selection of the descriptor which gives high performance is truly based on the dataset in hand. The dataset’s size, diversity, sparsity, complexity plays a critical role in the selection of the algorithm. These human engineered features when fed into the convolution networks improve their accuracy.

About Signzy

Signzy is a market-leading platform redefining the speed, accuracy, and experience of how financial institutions are onboarding customers and businesses – using the digital medium. The company’s award-winning no-code GO platform delivers seamless, end-to-end, and multi-channel onboarding journeys while offering customizable workflows. In addition, it gives these players access to an aggregated marketplace of 240+ bespoke APIs that can be easily added to any workflow with simple widgets.

Signzy is enabling ten million+ end customer and business onboarding every month at a success rate of 99% while reducing the speed to market from 6 months to 3-4 weeks. It works with over 240+ FIs globally, including the 4 largest banks in India, a Top 3 acquiring Bank in the US, and has a robust global partnership with Mastercard and Microsoft. The company’s product team is based out of Bengaluru and has a strong presence in Mumbai, New York, and Dubai.

Visit www.signzy.com for more information about us.

You can reach out to our team at reachout@signzy.com

Written By:

Signzy

Written by an insightful Signzian intent on learning and sharing knowledge.

 

Global digital trust system

How we replaced legacy banking processes with AI-driven technology

Posted on | by Signzian

Signzy — Building Global digital trust system using AI & Blockchain

One such interesting use case we encountered recently was about an id verification software. Given an image of an identity card the algorithm has to classify it to one of the following classes..

  1. Aadhaar
  2. PAN
  3. Driving License
  4. Passport
  5. Voter Id

In this blog post we will take you to behind-the-scenes of our state-of-the-art system and how we tackled the problem, ultimately overpassing the targeted accuracy required for real world use.

Knowing the beast we are to fight

As soon as we began to dive deeper into understanding the problem and identifying techniques we would use to attack it, we realised the most important constraints of the id verification software that we had to work within and the aim we are striving to achieve.

The idea is to deploy the pipeline into financial institutions with all possibilities of input variation and yet it should surpass or at least be equivalent to accuracy of a human being. The solution is to work on data which arrives from the most rural parts with pics taken from even 0.3 MegaPixel cameras and travelling over a dramatically slow connectivity. We knew the toughest challenge was to cater to variations that could arrive in inputs.

Humans have evolved intelligence for thousands of years, and created the systems to be easily processed by themselves. Take for instance an identity card. It is designed in dimensions to sit in pocket wallet, color formats to be more soothing to human eyes, data format which could sit well read by humans. If the Identity cards were designed to be consumed by a computer vision software it would have been an easier game, but since that’s not the case it becomes especially challenging.

We talked with different on-ground stakeholders to identify variations in input to the id verification software. Collecting initial samples wasn’t that hard, since a lot of these variations were told by our end users, but we knew creating training data is not going to be easy. We realized this quickly and started creating exhaustive training data in heavily curated and precisely controlled laboratory settings. We were able to get desired training sets successfully, which was half the problem solved.

World is not the cozy laboratory, we know that!

Our target was to create an id verification software which could be more than 99% accurate and yet be fast enough to make an impact. This isn’t easy when you know your input is coming from the rural end of India and you won’t have high end GPUs to process on (As a matter of fact, our largest implementation of this solution runs without GPUs).

 

A gist of environment where our input is created

The id verification app is expected to perform well in different sorts of real world scenarios like varying viewpoints, illumination, deformation, occlusion, background clutter, less inter-class variation, high intra-class variation (eg. Driving License).

You can’t reject an application by an old rural lady, who has brought you a photocopy of printout which in turn is obtained from a scanned copy of a long faded PAN card. We took it as a challenge to create the system so that it can help even the rural Indian masses.

A few samples that we expect as input into our system are here:

 

Fig(1): Few samples our expected input data

The number of samples we have for training is a huge constraint, you only have so much time and resources to prepare your training data.

Creating the id verification software

Baby steps ahead

We tried out various online identity verification methods for solving the problem. Firstly we extracted features using Histogram of Oriented Gradients (HOG) feature extractor from OpenCV and then trained a Support Vector Machine (SVM) classifier on top of the extracted features. The results were further improved by choosing XGBoost classifier. We were able to reach about 72% accuracy. We were using Scikit learn machine learning framework for this.

 

Not enough, let’s try something else

In our second approach, we tried ‘Bag of words’ model where we had built a corpus containing unique words from each identity card. Then we feed the test identity cards to an inhouse developed OCR pipeline to extract text from the identity card. Finally we input the extracted text to a ‘Naive bayes’ classifier for the predictions. This method boosted the accuracy to 96% . But the drawback of this approach was that it can be easily fooled by hand written text.

 

 

Taking the deep learning leap

“The electric light did not come from the continuous improvement of candles.” — Oren Harari

In the next approach we trained a classical Convolutional Neural Network for this image classification task. We benchmarked various existing state of the art architectures to find out which works best for our dataset eg. Inception V4, VGG-16, ResNet, GooLeNet. We also tried on RMS prop and Stochastic Gradient Descent optimizers which did not turn out to be good. We finalized on ResNet 50 with Adam optimizer, learning rate of 0.001 & decay of 1e-5. But since we had less data our model could not converge. So we did a transfer learning from “Image net”, where we used the existing weights trained originally on 1 million images. We replaced the last layer with our identity labels and freezed the remaining layers and trained. We noted that still our validation error was high. Then we ran 5 epochs with all layers unfreezed. Finally we reached accuracy of around 91%. But still we were lagging by 9% from our target.

 

Hit the right nail kid, treat them as objects

The final approach is where the novelty of our algorithm lies. The idea is to use an image object detector ensemble model for image classification purpose. For eg. the Aadhaar identity has Indian Emblem, QR code objects in it. We train an object detector for detecting these objects in card and on presence with a certain level of confidence we classify it as a Aadhaar. Like this we found 8 objects which were unique to each identity. We trained on state of the art Faster Region Proposal CNN (FRCNN) architecture. The features maps are extracted by a CNN model and fed into a ROI proposal network and a classifier. The ROI network tries to predict the object bounding box and the classifier (Softmax) predicts the class labels. The errors are back propagated by ‘softmax L2 loss function’. We got good results on both precision and recall. But still the network was performing bad on rotated images. So we rotated those 8 objects in various angles and trained again on it. Finally we reached an accuracy of about 99.46% . We were using Tensorflow as the tool.

Fig(7): FRCNN architecture from original paper

 

 

But we were yet to solve one final problem i.e the execution time. It took FRCNN approximately 10 seconds to classify in a 4 core CPU. But the targeted time was 3 seconds. Because of the ROI pooling the model was slow. We explored and found out that Single shot multibox detector (SSD) architecture is much faster than FRCNN as it was end-to-end pipeline with no ROI layer. We re-trained the model in this architecture. We reached accuracy of about 99.15%. But our execution time was brought down to 2.8s.

Fig(12): SSD architecture from original paper

Good work lad! What next?

While the pipeline we had come up with till here has a very high accuracy and efficient processing time, it was yet far from the a productionised software. We conducted multiple rounds of quality checks and real world simulation on the entire pipeline. Fine-tuning the most impactful parameters and refining the stages, we have been recently been able to develop a production ready, world class classifier with an error rate less than human and at a much much lesser cost.

We are clearly seeing the impact deep learning can have on solving these problems which we once were unable to comprehend through technology. We were able to gauge the huge margin of enhancement that deep learning provides over traditional image processing algorithms. It’s truly the new technological wave. And that’s for good.

In the upcoming posts, we will share our story on how we tackled another very difficult problem — Optical Character Recognition (OCR). We are competing with global giants in this space including Google, Microsoft, IBM and Abby and clearly surpassing them in our use cases. We have a interesting story to tell over “How we became the global best in enterprise grade OCR“. Stay tuned.

Thank you.

Signzy AI team

Be part of our awesome journey

Do you believe that the modern world is driven by bits and bytes? And think you can take it on? We are looking for you. Drop us a note at careers@signzy.com.

Summary view

  1. Real world is not your laboratory, training data needs to be diverse and needs better outlier handling
  2. Deep learning requires you to be patient but once it starts getting effective it gives your exponential returns
  3. In a narrow use case you can beat a global giant with all the computing power in the world.

So future of deep learning is not commoditized products but adoption of deep learning in use cases as a tool to bring intelligence across the board. Deep Learning has to be company culture and not just a ‘tool’.

About Signzy

Signzy is a market-leading platform redefining the speed, accuracy, and experience of how financial institutions are onboarding customers and businesses – using the digital medium. The company’s award-winning no-code GO platform delivers seamless, end-to-end, and multi-channel onboarding journeys while offering customizable workflows. In addition, it gives these players access to an aggregated marketplace of 240+ bespoke APIs that can be easily added to any workflow with simple widgets.

Signzy is enabling ten million+ end customer and business onboarding every month at a success rate of 99% while reducing the speed to market from 6 months to 3-4 weeks. It works with over 240+ FIs globally, including the 4 largest banks in India, a Top 3 acquiring Bank in the US, and has a robust global partnership with Mastercard and Microsoft. The company’s product team is based out of Bengaluru and has a strong presence in Mumbai, New York, and Dubai.

Visit www.signzy.com for more information about us.

You can reach out to our team at reachout@signzy.com

Written By:

Signzy

Written by an insightful Signzian intent on learning and sharing knowledge.

 

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