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!

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.

 

Security

Security in a digital world!

Posted on | by Signzian

Security in a digital world has become paramount as our personal, financial, and professional lives increasingly shift online. The rapid proliferation of digital technologies, while offering immense convenience and connectivity, also brings forth a plethora of challenges in safeguarding sensitive data and maintaining privacy. Cyberattacks, identity thefts, and data breaches are becoming more sophisticated, emphasizing the need for robust cybersecurity measures. Individuals, corporations, and governments alike are recognizing the imperative of bolstering their digital defenses, ensuring that as we embrace the conveniences of the digital age, we’re not compromising our security and integrity. In essence, as we navigate this digital era, being cyber-aware and proactive in our security measures is not just an option, but a necessity.

Bashing passwords as vulnerable means of online security is quite common these days. Sure — authentication means like biometrics, OTP, mobile, etc., do sound fancy and are touted as cornerstones in future security practices. But fundamentally there is nothing wrong with a password paradigm. In fact, it’s the weakness of individual passwords that leads to a security risk.

In this article, we are going to give you a background to passwords, their philosophical underpinning, and also evaluate the other possible options we have.

Passwords have a long history. They are used to access private accounts, applications, documents, databases, websites and more since long. Even the treasure den in the fabled tale of Ali Baba and the Forty Thieves had a password! The other way to access such secrets was through some body tattoo or possession of a unique seal.

Interestingly, these three ancient methods of verification still do represent the fundamental principles of modern authentication practices:

  1. What you know — Passwords/PIN
  2. What you have — Seal/OTP/Credit Card/Tokens
  3. Who you are — Biometrics/Body tattoos

The combination of these three factors (3FA) is seen to represent an authentication framework for accessing information or doing risky transactions. Take an example of a Credit Card swipe. The card represents “what you have” and the pin represents “what you know”. Combining the two provides greater security than any one method alone. When any two of these are used, it’s called two-factor authentication. More factors imply higher security.

What is often not discussed is which factors are safer in which contexts. Given we are moving into rapid digitization it might be important to discuss the three factors, their types and when should they be used.

Let us trace this movement from password based to other factors and see what maybe a good framework to keep consumers and systems safe.

How passwords work?

Passwords are stored in a system as hashes.

A hash is a one-way pseudo-random function, which means that it can produce a random text from a password.

But the random text can’t reproduce the original password.

Let’s take an example of SHA-2 Hash algorithm.When we feed it a password, say “ankit8388”, it produces a random text like “96c32e63d785c77d8de8089523a346210d2299a25c349c518dc8bf0181ff911b”. This hash is now stored in the database and with it the website can authenticate me without ever storing my original password.

(Even when the database is hacked, my password doesn’t get leaked because the original data is never saved in a database.)

How hackers hack passwords?

To hack passwords, hackers create pre-created hash tables for all possible password combinations.

For the “ankit8388” password, a hash table of small letters and numbers of length 9 would be able to find a match.

This means the hacker will need to process all the possible permutations and combinations of small letters (26) and numbers (10) for 9 places. In mathematical terms this would be (10+26)⁹ combinations. This is a highly intensive task and a single computer might still take 50 years to do this.

But hackers work together and pool resources, which means 50 hackers with their computers can create such a table in less than a year.

Further, it’s possible that they will find a match at a half-way stage or within 6 months.

The point is this:

A password becomes unsafe when it’s too short and simple to guess or crack.

Alternatively, if a user sets a complex, multi-character long password, there’s a risk the user will keep it noted somewhere (and this note might reach unsafe hands and cause a vulnerability).

So passwords (either too simple or too complex) can be unsafe in their own ways. That said, the other authentication means available, too, aren’t foolproof. Lets get a bit more understanding on other authentication methods.

Why biometrics and OTPs can’t be the foolproof solutions for the Digital Security?

The two emerging contenders for future digital authentication are biometrics and OTPs.

Biometrics, along with a password, would indeed enhance security by providing a two-factor authentication. But when used alone, it’s not the best bet for the future because it comes with three big problems:

  1. Unlike passwords, biometric data cannot be stored as a hash. This means that the web application will need to store your biometric data as is. This is a very risky proposition as, in case of a hack, your actual biometric data (or its mathematical representation, in some cases) is revealed. In one of the biggest data breaches in the US, 5.6 million fingerprints of government employees got hacked from the the U.S. POM (Office of Personnel and Management), which gave the hackers access to raw biometric data.
  2. In case biometric data is ever compromised, there is no resetting like a password. This means, you would forever be prevented from using your biometric authentication during your lifetime.
  3. Biometric systems are extremely susceptible to spoofing. In spoofing, a stolen digital template of a biometric trait could be inserted into the authentication process to authenticate the wrong user. In 2013, Jan Krissler, a famous German hacker spoofed Apple’s Touch ID (iPhone 5S) on the other day of it’s release. He used the smudge on the screen of an iPhone to print a dummy finger using wood glue and sprayable graphene. He then used this print to successfully unlock a phone registered to someone else’s thumb. The same hacker then used high-resolution photos of Ursula von der Leyen, Germany’s Minister of Defence, to beat fingerprint authentication technology.

OTP, as an alternate authentication means, has its own set of risks:

An OTP is a one time password consisting of characters, numbers or symbols that’s used to authenticate a user for a single login session. And it becomes invalid after a few seconds.

Take an example of a credit card swipe as I’ve explained earlier. (The card represents “what you have” and the pin represents “what you know”). When you swipe the card you get a code ( an OTP) and you aren’t authenticated until you enter the code and are verified.

So, here two authentication methods are being used for authentication (two factor authentication) which ensures more security. But still they can’t be considered as the best security solution.

  1. The biggest challenge to the OTP authentication factor comes from trojan software.

Hackers show their victims a browser pop-up box or ad that looks like an authentic message from the bank and prompts the user to download a “security application” or a “mobile banking application” on their phones.

Once a user downloads such fake applications, hackers can easily intercept their SMSes. Which allows the hackers to read the OTPs sent on the mobiles.

Security expert, Brian Krebs, tells how an Android botnet targeting banks in the Middle East could infect more than 2,700 phones and intercept at least 28,000 text messages:

This attack affected customers from various banks including the ones from the Riyad Bank, SAAB, AlAhliOnline (National Commercial Bank), Al Rajhi Bank, and Arab National Bank.

 

2. SIM swap/cloning: By procuring a duplicate SIM card in a user’s name, hackers can use it to receive communication from the banks (including the OTPs).

3. Social engineering: Hackers also call users claiming to be from the bank. And during the call, they ask for the OTP. Unsuspecting users are usually easy victims to such attacks.

4. SS7 Attacks: Using flaws in Signaling System 7 (SS7) hackers can listen to private phone calls and read text messages of the users. According to a report from German-language newspapers Süddeutsche Zeitung, in a cyber attack in Germany hackers intercepted OTP’s using SS7 flaws and stole customer’s money from their accounts.

As you just saw, all the three authentication factors — passwords, biometrics, and OTPs — have their set of risks. However, passwords stand out because users can exponentially strengthen their passwords (while also keeping them easy to remember). So let’s re-examine passwords and see how we can improve them, and then explore the Password 2.0 approach.

How passwords can be made more secure?

As we discussed earlier hackers have been able to pool resources and pre-create hash tables hence making guessing of simple passwords really easy. Then what could be the way to make their life hard? Increase the combinations, of course. And the usual way of doing it has been to increase possible inputs:

  • Alphabet (Small letters and caps) — 52
  • Numbers — 10
  • Special characters — 33

So this gives a total combination of 95 characters. Cracking this is so hard that it would take the same hacker group over 6000 years to hack password in the same way. And at that point, I obviously don’t care (unless AI leads to afterlife; another topic for another blog :))

Therefore, from a security guy’s point of view, all these rules of having multiple combinations is really helpful because it keeps you safe. But at the time of signing up or using a service, this becomes a huge pain and a turn off. Also, it’s an eventual security risk as people keep forgetting such tough passwords and hence often note it down in insecure places, such as desktop files or random pieces of paper.

Introducing Password 2.0 — the Paraphrasing Approach (the security and user-friendly password solution)

Now, there is another way to do this, which seemed to have been neglected until now: the length of the password. I could have achieved a similar tough password by simply having 4 more characters, i.e., a 13-letter-long password, without any restriction on small letters, caps, numbers, special characters, etc.

This new paradigm is what I call Password 2.0: the passphrase approach. It’s easy to remember a passphrase, such as “thisisacoolpassphraseforthiswebsite”. Such passphrases can provide a better user experience at the time of signing up and also during authentication.

Also, at its length (35 characters), hash tables will be almost impossible to compute. Thus we can build passwords that are convenient yet secure.

Why passwords are crucial for Security?

One principle that has to be accepted in a security paradigm is — you will get hacked. This principle is important to remember when choosing one or a combination of the three authentication factors (passwords, biometric or an OTP).

The property of biometrics in this context is really risky. As biometrics can never be changed, once hacked they become vulnerable for that person for their lifetime. So in a biometric auth world, over time more and more people would get vulnerable. Thus you would inevitably reach a stage where, for a certain population, biometric will not be a valid authentication mechanism.

Mobile phones, or number can also not be changed very frequently or easily and hence make changing of the auth factor difficult.

Unlike biometrics and mobile numbers (or handsets), passwords can be changed if they get hacked. That too quite easily. Hence they have no permanent vulnerability. Another great property they have is the ability to protect the actual password at each authentication. This paradigm is akin to knowing a secret that you will never reveal but are able to prove you know it.

So while biometric and OTP authentication breaches leave their users vulnerable (for life), passwords breaches always give the users a way to “reset”. Because of their simplicity and cryptographic beauty, passwords will continue to dominate as the higher security layer. And when you add an additional layer of authentication to a password (like biometric or an OTP), you can probably design a more secure system. (In a further article we will go through the best combination given a business use-case)

The password 2.0 approach — of creating complex but easy-to-remember “secret-style” passwords — can be a useful tool in such a scenario where the password is a mainstay in the security authentication mix. So, start thinking of a secure passphrase because in a modern digital world, “a strong secret” will be worth more than any other assets you own.

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.

 

Fintech & Banks

Fintech & Banks – a synergistic approach!

Posted on | by Signzian

Fintech & Banks, once perceived as rivals in the financial ecosystem, are now increasingly embracing a synergistic approach to reshape the future of finance. While fintech startups bring agility, innovation, and digital prowess to the table, traditional banks offer a wealth of experience, deep-rooted customer trust, and robust regulatory frameworks. By collaborating, they harness the best of both worlds: leveraging cutting-edge technology to enhance customer experiences while building on the strong foundations that banks have established over centuries. This evolving partnership not only promises greater financial inclusivity and efficiency but also indicates a future where technological innovation and banking legacy coalesce for the betterment of consumers worldwide.

The global economic crises brought the world economy to its knees a decade ago. Still, the world’s largest banks operated and continue to operate, almost as if they were too big to fall. This feeling was echoed by Governments the world over. Their steadfast foundation among consumers has now been challenged by a different type of institution. This is the financial technology start-ups. (fin-techs.)

A compelling argument elucidates the whirlwind-like effect that fin-tech start-ups have on banking. The shift of priorities towards a consumer-convenience model gives incentive to banks to collaborate with fin-tech companies. These fin-techs make banking processes quicker and easier. They continuously innovate in the field to ensure greatest satisfaction of the consumer.

Fintech – a Growing Force Internationally

The growth of fin-techs has been exponential, making them a true force to be reckoned with. Venture capitalists, private equity firms, corporates etc have poured an unprecedented amount of money into global financial technology start-ups. More than $50 billion has been invested in almost 2,500 companies since 2010. These innovators redefine the way we store, save, borrow, invest, move, spend and protect money. Leading financial analysts and experts like KPMG have estimated that the investment in fin-techs will increase by a whopping 36% in 2016.

 

There are various instances of success when it comes to fin-techs across the globe. Fin techs realize that consumer desire is paramount. They have persevered to provide quality digital service to thousands of consumers across the globe. M-Pesa is a case in point. M- Pesa is mobile money platform created by Vodafone and functional in Kenya. It capitalized on the realization that phones can be used to not only make calls, but also execute financial transactions. The premise of the M-Pesa is that most people in emerging and frontier markets don’t have bank accounts. They can use the platform via their mobile phones to make payments and ease money transfers. Estimates suggest that nearly 43 percent of the gross domestic product of Kenya takes place on the M-Pesa platform. The upward trajectory of investment in fin-techs is due to the satisfaction they provide to consumers worldwide. This has ensured that they become a strong global force.

Fintech in India

The Modi regime in India has been particularly supportive of the start-up culture in India. There has been consistent lobbying for foreign direct investment into the entrepreneurship sector. There have been initiatives such as ‘Start-up India, Stand up India’. India is among the first five largest start-up communities in the world. with the number of start-ups crossing 4,200 (at a growth of 40%) by the end of 2015. A Microsoft Ventures report states that the number of start-ups is expected to zoom from 3,100 in 2015 to an expected 11,500 start-ups by 2020.

The recent demonetization has encouraged ideas of a cashless and an e-wallet friendly economy. It has further prioritized the necessity of secure enablers and other platforms, such as Signzy Technologies Pvt. Ltd. They ensure a simple, secure and legal way of making payments. They also help to execute other necessary due diligence through their products like RealKYCTM, ARITM (Algorithmic Risk Intelligence) and Digital Contracts. This provide safety and security of parties to an online/diligence related transaction.

Flipkart CEO Binny Bansal and Snapdeal (and Freecharge) CEO Kunal Bahl acknowledged demonetisation as a game-changer. They labelled it as a move which will usher in the era of digital growth in India’s economy. The economy currently needs a stable and reliable platform to ease payments and related transactions now. This is where fin-techs step in. They bridge the gap between security and dependability. These are two key considerations that consumers in this new-look economy will have.

Services which Fin-Techs Can Offer

  • Mobile Payments

Payment security is a key concern in today’s risk-loaded environment. Innovation is essential to ensure risk mitigation and consumer responsiveness in the sector.[6] Fin-techs enable convenient and quick payments for various services, goods and other transactions through mobile wallets. Tokenisation and biometric data have developed to a great extent. This ensures authorization of payments through ‘mobile wallets’. There is no need to go through elaborate documentation and technicalities anymore.

  • P2P Payments

Fin-techs also enable the transfer of value of currency between to persons, thus enabling person to person (P2P) payments. The same transactions can be made between institutions and persons also.

 

The picture above is a representation of a report by BI Intelligence. It shows the growing popularity of P2P payments using mobiles. It also explains how they may serve as a bridge to widen use of smartphones to complete in-person “wallet-less transactions”.

Collaborative Actions between Fin-techs and Banks in India

Banks have realized that the way forward is to embrace digitized processes. It is essential to collaborate with fin-tech start-ups to maximize consumer satisfaction. Various Indian banks have taken active steps to partner with start-ups to make banking processes easier.

  • HDFC Bank

HDFC has partnered with a Bangalore-based start-up called “Tone Tag”. It provides phone-based proximity payment services to its customers. it had also tied up with Chillr — an app-based payments platform which transfers funds from account to account without having to fill in any account numbers or bank codes. The bank is also known to host start-up competitions in the form of digital innovation summits.

  • Axis Bank

Axis bank partnered with Vayana Network. Together. Together, they launched “Invoice to Payment,” an end-to-end digital invoicing and payment solution. The solution aims to simplify B2B payments in India. Currently B2B payment is estimated at over $95 billion annually. It offers digital invoicing, electronic workflow approval and instant payment processing for businesses in India.

  • ICICI Bank

ICICI Bank is partners with Paytm. Paytm is India’s largest mobile payment firm to launch virtual prepaid cards. This idea has now evolved into the Paytm wallet. It can be used for purposes such as purchasing supplies, usage of public transport etc.

YES Bank has partnered with some exciting fin-tech start-ups like Ultracash Technologies. They have launched payments processing through sound waves and TimesofMoney. They also plan on launching their own online remittance solution called YES Remit. YES Remit will allow non-resident Indians (NRIs) to send money to any YES BANK account or other bank accounts in India.

Bill Gates once said that banking would remain essential to the world, but banks wouldn’t. Fin-tech companies are looking to take over the market. Hence, NBFCs and financial institutions need to remember is that collaboration is key. It is high time that these institutions look inwards and identify key weaknesses that these fin-techs can fill proficiently. Fin-techs and undeniably exciting. They also own the brightest and most innovative minds in the country and have enough funding. Yet taking lessons in market expertise, brand image, expensive licensing, brand name and image etc. from banks is the only way for them to progress. A combination of digitally aware customers and a symbiotic synergy between fin-techs and financial institutions is indicative of the birth of a new system of global finance.

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.

 

Cryptography: The Vault for Today’s Banks

Posted on | by Signzian

Analyzing the importance of cryptography in ensuring online security.

When recently Edgartown bank in Massachusetts, USA needed more space they made a decision to do away with their steel enforced vault built in 1850. What seemed to be a simple re-furnishing task turned into a mammoth demolition exercise! Its only when they started digging deep, did they realise that it wasn’t that the vault was put in the Bank. But The bank was built, around the vault. Thus removing the vault meant destabilizing the complete infrastructure.

This small instance reveals a very important aspect of Banking. Safety is paramount. Banks have constantly been the biggest buyers of safe and vaults. Even today, banks pay tremendous attention to detail as regards safety and vaults, like the Federal Reserve Bank of New York, which claims to possess one of the world’s safest vaults. [1] The vault in New York is safeguarded by a comprehensive multi-layered security system, highlighted by a 90-ton steel cylinder protecting the only entry into the vault. The nine-foot-tall cylinder is set within a 140-ton steel-and-concrete frame that, when closed, creates an airtight and watertight seal. [2] In light of prevalent practices such as net banking, e-wallets and digital payment systems, the importance of security is further amplified.

Banks have always thrived (and done maximum business) on the notion of trust that customers place in them. Direct evidence of this principle can be found in the fact that banks act as trustees and guardians of the currency of their customers. Customers deposit large sums of money and are led to believe that a similar value of currency (as regards their bank balance) is present at the bank, despite the fact that it is common knowledge that banks often deal with monetary values and transaction amounts which are far greater than the actual amount of currency present at the bank at a particular point of time.

Need for Security

When the infamous thief Willie Sutton was asked why he robbed banks, he answered, “Because that’s where the money is.” While the witty comeback still “holds up” today, the weapon of choice now is more likely to be a pen/computer than a gun. The business of a bank/financial institution is constantly under threat from menaces of robbery, or even fraud. What is pertinent to note, is that banks have always placed tremendous value on security and will leave no stone unturned to ensure that safety standards remain high. [3]

The advent of technology has made fraud-inducing practices more prevalent and sophisticated, with them being at an all-time rise.[4] A survey on financial trends made by Assocham and PwC said that financial frauds led to approximately $20 billion (Rs 1.26 lakh crore) in direct losses annually. D S Rawat, Secretary-General, Assocham stated that “Financial fraud is big business, contributing to an estimated $20 billion in direct losses annually. Industry experts suspect that this figure is actually much higher, as firms cannot accurately identify and measure losses due to fraud. The worst effect of financial frauds is on FDI (foreign direct investment) inflows into India.” [5]

The report states that as 75% of the population of India has a mobile phone, ‘banking on the go’ has become the norm, so as to increase the convenience to the consumer. Which reflects in the Reserve Bank of India’s data which states that from a meagre INR 1819 crore in 2012, the volume of mobile banking transactions has risen to INR 1,01,851 crore in 2015.

Technology continues in the race with bank robbers, coming up with new devices such as heat sensors, motion detectors, and alarms. Bank robbers have in turn developed even more technological tools to find ways around these systems. Although the number of bank robberies has been cut dramatically, they are still attempted. [6]

Cryptography

As the world moves digital there is a corresponding need of similar safety and security in the digital world. Cryptography plays a crucial role in ensuring complete safety in areas like e-mail to cellular communications, secure Web access and digital cash. Cryptography helps provide accountability, fairness, accuracy, and confidentiality. It can prevent fraud in electronic commerce and assure the validity of financial transactions. [7]

Cryptography secures the global information infrastructure by encrypting data flows and protecting data from third-party interception. Nowadays, cryptography secures data in transit and at rest, protects personal information and communications, and ensures the integrity of every online purchase. Cryptography has four key attributes:

1. Confidentiality: The protection of information and prevention of unauthorized access;

2. Privacy: Protecting the personal information of individuals;

3. Non-repudiation: The inability to deny an action took place; and

4. Integrity: Assurance that information cannot be manipulated. [8]

Cryptography also powers one of the most rapidly rising finance technology — Blockchain.

It has driven businesses to reimagine how their networks operate and has become synonymous with alternative business models. At its core, however, blockchain leverages a vast amount of public key cryptography to enable confidentiality, privacy and security of data and user identities. [11] Apart from its security benefits, blockchain also increases the speeds of different transactions. Instead of waiting days for a check to clear, a payment can be verified in seconds. There’s also less risk that payments will have to be denied because funds are unavailable. There’s no more “playing the float” since account debits and credits are instantaneous. [12]

Conclusion

Banks in India have started realizing that consumer experience and ease of banking are very important. This has led to several collaborations between the fin-tech start-ups and Banks. What would probably be the next wave in this collaboration is startups that focus on digital security helping banks bring the “offline” trust to the online world. Banks which focus on security and safety of digital consumers are more likely to build trust in the long run, and would most probably be the winners in the digital world.

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 ]

 

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