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Tech in Asia - Connecting Asia's startup ecosystem

Fintech startup BharatX acquires healthcare finance firm Zenifi to enter medical lending. Zenifi CEO Padam Kataria joins BharatX to lead healthcare lending vertical.

BharatX: BharatX expands into healthcare finance with acquisition of Zenifi - The Economic Times

Bharat Electronics Limited (BEL) is an Indian Government-owned aerospace and defence electronics company. It primarily manufactures advanced electronic products for ground and aerospace applications. BEL is one of sixteen PSUs under the Ministry of Defence of India.

Bharat Electronics - Wikipedia

Bharat Petroleum is among the leading oil and gas companies in India. The organisation aims to provide only the purest oil in India by virtue of using its own highly developed refineries.

Oil & Gas Companies in India |Top Petroleum Companies | Petroleum ...

BHEL is one of the largest engineering and manufacturing companies of its kind in India engaged in design, engineering, construction, testing, commissioning and servicing of a wide range of products and services with over 180 product offerings to meet the ever-growing needs of the core sectors ...

Official Website of Bharat Heavy Electricals Limited, New Delhi, ...

Bharat Forge is one of the leading forging companies in India. A preferred technology and engineering driven development partner and global leader in metal forming, Bharat Forge serves several sectors including Automotive, Railways, Aerospace, Marine, Oil and Gas, Power, Construction and Mining.

Bharat Forge - One of the Leading Forging Companies in India

भारत सरकार GOVERNMENT OF INDIA · A Govt of India Enterprise, Ministry of Defence

Home Page | BHARAT DYNAMICS LIMITED INDIA

BharatX | 36,417 followers on LinkedIn. Redefining Credit for the Indian Middle Class. | BharatX is a VC Backed early-stage FinTech startup that uses technology to empower the Indian Middle Class with credit. Approximately 110 Million Households whose income is more than 5 lakh per annum, do ...

BharatX | LinkedIn

Fintech startup BharatX has raised $4.5 million in a funding round from Silicon Valley-based famed accelerator Y Combinator, 8i Ventures, Multiply Ventures and Soma Capital. The company plans to utilise the funds to expand its team and develop its product.

BharatX Funding: Fintech startup BharatX raises $4.5 million in ...

The funding will help drive BharatX&#x27;s delivery of seamless Embedded Credit experiences on many consumer-facing platforms, with zero credit risk and zero operational headaches for the platforms

Fintech firm BharatX raises $250,000 in pre-seed round led by Java ...

BharatX

bharatx

The Binance Build for Bharat hackathon - by , the global blockchain company behind the world’s largest digital asset exchange and blockchain protocol - Binance Smart Chain, announced Semiott Protocol, and as the top three winners. This was the first DeFi-focused Hackathon in India, based on the vision of the initiative - : an initiative designed under the . The Hackathon also gained the support of large scale tech players: ( Previously Matic Network ), , , . Binance Whrrl GoSats Polaris Accelerator Programme Blockchain for India Fund Google Cloud, Polygon Marlin Protocol Band Protocol Ankr cloud infrastructure The winners were selected from a pool of 350+ developers and startups that had applied for the Build for Bharat programme and were challenged with multiple tasks including a 48 hours long hackathon and Binance Smart Chain integrations. The top 8 finalists battled it out at a virtual Demo Day with participants attending from across the globe. The event also saw an opening address by Changpeng Zhao, CEO, Binance and a panel discussion featuring Nischal Shetty, CEO, WazirX , Ryan Fang, Coo and Co-Founder, Ankr, Pratik Reddy, Head of Research at Marlin Protocol and Raghu Mohan, CEO, Lumos labs. The winner of the Hackathon, the Semiott Protocol by , a serial entrepreneur, is an algorithmic stablecoin for value veracity. , the first runners up, is a blockchain platform connecting custodian of assets (warehouse) and banks with integrated commodity trading platforms. Gokul Alex Whrrl , the second runners-up, is a Bitcoin stacking app that lets users earn bitcoin rewards when they shop with partnered merchants. GoSats The Top 3 winning projects will secure prizes from a pool of 40,000 USD that includes Google Cloud Credits. “This hackathon has been an interesting and enriching experience. We are happy to provide the catalyst for the growth of the DeFi economy in India.” said Changpeng Zhao (CZ), CEO at Binance. “The disruptive projects and ideas that we have seen through this engagement has been a testimony to India’s readiness for a decentralized economy and at Binance we are happy to provide the necessary fuel to augment this innovation.” he added. Build for Bharat aimed to identify creators, innovators, startups, and developers from all over India working on unique and creative products utilizing digital assets and blockchain technology around decentralised finance (DeFi) such as mini-Tokens, micro financing products, blockchain datasets, blockchain-AI solutions and more. Additionally, this month Binance launched the under the $100M fund set up to connect DeFi and CeFi. The program aims to help BSC-based startups gain industry exposure and accelerate their business growth. Participants from existing hackathons, including Build for Bharat can participate in the programme. Applications for the first round of MVB Programme will close by 28th Feb. Most Valuable Builder Programme

Meet the Winners of Binance's Build for Bharat Hackathon 

Episode 17: An interview with Bharath Rao, CEO and founder of CryptoDisrupted http://LEVERJ.io Trent interviews Bharath Rao, CEO of . In the interview we discuss decentralized exchanges, derivatives trading, the 2008 market crash, and the future of crypto trading. http://LEVERJ.io Also . available on iTunes For more episodes of , or . Crypto Disrupted subscribe on YouTube subscribe and listen on iTunes

Decentralized Leveraged Exchange with Bharath Rao

Hacktivists Take on Misinformation in a New Internet Age

Startups of The Year 2024 Winners: Asia

الفوركس الفوركس 2024 الفائزين: آسيا

২০২৪ সালের বিজয়ী উদ্যোক্তা: এশিয়া

Startups des Jahres 2024 Gewinner: Asien

Startups del Año 2024 Ganadores: Asia

Startups nan ane a 2024 genyen: Azi

2024年のスタートアップ 優勝者:アジア

2024-ийн Startups Шагнагч: Ази

Startup Tahun 2024 Pemenang: Asia

Startups do Ano 2024 Vencedores: Ásia

Startups of The Year 2024 Waqtiga: Asia

Стартапи року 2024 Переможці: Азія

Startups of the Year 2024 laureatlar: Asia

Authors:
(1) VINÍCIUS YU OKUBO, Dept. Electronic Systems Engineering, Polytechnic School, University of São Paulo, Brazil;
(2) KOTARO SHIMIZU, Department of Applied Physics, The University of Tokyo, Tokyo 113-8656, Japan;
(3) B. S. SHIVARAM, Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA;
(4) HAE YONG KIM, Dept. Electronic Systems Engineering, Polytechnic School, University of São Paulo, Brazil. Authors: Authors: (1) VINÍCIUS YU OKUBO, Dept. Electronic Systems Engineering, Polytechnic School, University of São Paulo, Brazil; (2) KOTARO SHIMIZU, Department of Applied Physics, The University of Tokyo, Tokyo 113-8656, Japan; (3) B. S. SHIVARAM, Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA; (4) HAE YONG KIM, Dept. Electronic Systems Engineering, Polytechnic School, University of São Paulo, Brazil. Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Related Work II. Related Work III. Methodology III. Methodology IV. Experiments and Results IV. Experiments and Results V. Conclusion and References V. Conclusion and References IV. EXPERIMENTS AND RESULTS A. PERFORMANCE EVALUATION A. PERFORMANCE EVALUATION A. PERFORMANCE EVALUATION Evaluating TM-CNN poses a certain challenge because there are no benchmarks or published results for direct comparison. So, we defined that junction and terminal detections reviewed by human observers as the gold standard and compared the algorithms against this standard. For testing purposes, we annotated 5 independent images from varying experiments, using the same procedure as the training images (subsection III-E). False detections were excluded since they do not contribute to the detection evaluation. In total, the test set contained 3,551 junctions and 3,653 terminals. B. RESULTS FROM A PHYSICS PERSPECTIVE B. RESULTS FROM A PHYSICS PERSPECTIVE B. RESULTS FROM A PHYSICS PERSPECTIVE In Bi:YIG films, it is known that a perfect stripe pattern is the most energetically favorable and stable configuration of magnetic moments [4]. In this structure, the magnetic moments exhibit stripes aligning straight in one direction throughout the entire structure, resulting in large spatial coherence. Meanwhile, the experimentally obtained structures often manifest labyrinthine patterns, as illustrated by dark and bright stripes in Fig. 1. In these labyrinthine patterns, stripes are aligned within small domains but propagate in different directions between different domains, and thus the spatial coherence of stripes is smaller than the perfect stripe. Notably, junctions and terminals are defects emerging in the formation of these labyrinthine patterns, intricately linked to the growth of spatial coherence in the system. Introducing such defects into the perfect stripe structure leads to the bending and branching of the initially aligned stripes, eventually reducing spatial coherence accompanied by the transformation to labyrinthine patterns. Indeed, a plethora of defects are observed in the labyrinthine patterns shown in Fig. 2. Conversely, the elimination of these defects plays a crucial role in the transition from a quenched state to an annealed state, where the system increases the spatial coherence. Each of these defects cannot be eliminated alone by continuous deformation of the stripes due to their topological properties. They can be removed through the pair annihilation. For instance, in Fig. 2, terminals are branched from junctions and they can be removed pair wisely by reducing the branching distance. These defects, known as topological defects, are widely recognized for playing a crucial role in the formation of periodic structures. By detecting the positions of defects, the evolution of the labyrinthine patterns would be systematically quantified. Specifically in the context of magnetic labyrinthine patterns, a much smaller dataset was used to detect these defects manually [4]. Recently, persistent homology has been used to extract the topological features of the labyrinthine patterns in a systematic way [38]. However, it is still difficult to detect the positions of defects and classify them into junctions and terminals. In this article, we achieved the systematic detection. To quantify the evolution of the labyrinthine patterns, we applied the TM-CNN technique to the 444 images and investigated how the number of defects and their locations changes step by step. Fig. 6 illustrates the step dependence of the number of junctions and terminals during the demagnetization process. Results from 6 different experimental runs were averaged to estimate the number of defects. In the initial stages of the demagnetization process, there are approximately 800 defects. The number of defects sharply decreases around step 5 and remains nearly unchanged from step 10 onward. Consequently, the transition to the quenched state is inferred to start before step 5 and be completed by step 10. The reduction in the number of defects during the demagnetization process is compatible with the naive expectation based on the physical argument of a decrease in the number of defects associated with the increased spatial coherence of the stripes. Furthermore, the number of junctions and terminals are close, which is consistent with the topological argument that junctions and terminals are paired. Further quantification of the evolution of the labyrinthine patterns based on the spatial distribution of the defects (and not only on their number) is discussed in another article [6]. V. CONCLUSION In this work, we presented a new algorithm named TMCNN to detect defects in magnetic labyrinthine patterns, contributing to a pioneering analysis in material science. Our study characterized the evolution of junctions and terminals in magnetic stripes during demagnetization procedures, aiming at better understanding defect arrangement in magnetic materials [6]. TM-CNN employs a two-stage detection procedure, combining template matching for initial detection and a convolutional network classifier for refining misdetections. This approach ensures a high detection accuracy and facilitates dataset annotation through a semi-automatic procedure. In our experiments, TM-CNN exhibited performance superior to other techniques, achieving an impressive F1 score of 0.988. This high performance is mainly due to TMCNN’s ability to locate small and clustered objects. TM-CNN achieves almost 100% accuracy with a simple CNN classifier with less than half a million parameters and can be used even on computers without GPUs. While TM-CNN was developed for defect detection in labyrinthine magnetic patterns, its potential applications are not limited to this field. Future research could explore the use of TM-CNN in other domains, such as identifying bifurcations in blood vessels or adapting it to other structures that can be modeled using templates. REFERENCES [1] Masaya Uchida, Yoshinori Onose, Yoshio Matsui, and Yoshinori Tokura. Real-space observation of helical spin order. Science, 311(5759):359–361, 2006. [2] T. Garel and S. Doniach. Phase transitions with spontaneous modulation the dipolar ising ferromagnet. Phys. Rev. B, 26:325–329, Jul 1982. [3] P. Molho, J. L. Porteseil, Y. Souche, J. Gouzerh, and J. C. S. Levy. Irreversible evolution in the topology of magnetic domains (invited). Journal of Applied Physics, 61(8):4188–4193, 04 1987. [4] M. Seul, L. R. Monar, L. O’Gorman, and R. Wolfe. Morphology and local structure in labyrinthine stripe domain phase. Science, 254(5038):1616– 1618, 1991. [5] Naoto Nagaosa and Yoshinori Tokura. Topological properties and dynamics of magnetic skyrmions. Nature Nanotechnology, 8(12):899–911, Dec 2013. [6] Kotaro Shimizu, Vinicius Yu Okubo, Rose Knight, Ziyuan Wang, Joseph Burton, Hae Yong Kim, Gia-Wei Chern, and B. S. Shivaram. Machine Learning Assisted Characterization of Labyrinthine Pattern Transitions. arXiv:2311.10558, 2023. [7] Kai Briechle and Uwe D. Hanebeck. Template matching using fast normalized cross correlation. In David P. Casasent and Tien-Hsin Chao, editors, Optical Pattern Recognition XII, volume 4387, pages 95 – 102. International Society for Optics and Photonics, SPIE, 2001. [8] P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, volume 1, pages I–I, 2001. [9] N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 886–893 vol. 1, 2005. [10] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. 25, 2012. [11] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition, 2015. [12] Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation, 2014. [13] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88(2):303–338, June 2010. [14] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks, 2016. [15] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection, 2016. [16] Yang Liu, Peng Sun, Nickolas Wergeles, and Yi Shang. A survey and performance evaluation of deep learning methods for small object detection. Expert Systems with Applications, 172:114602, 2021. [17] Ricardo H Maruta, Hae Yong Kim, Danilo R Huanca, and Walter J Salcedo. A new correlation-based granulometry algorithm with application in characterizing porous silicon nanomaterials. ECS Transactions, 31(1):273, 2010. [18] Hae Yong Kim, Ricardo Hitoshi Maruta, Danilo Roque Huanca, and Walter Jaimes Salcedo. Correlation-based multi-shape granulometry with application in porous silicon nanomaterial characterization. Journal of Porous Materials, 20:375–385, 2013. [19] Sidnei Alves De Araújo, Jorge Henrique Pessota, and Hae Yong Kim. Beans quality inspection using correlation-based granulometry. Engineering Applications of Artificial Intelligence, 40:84–94, 2015. [20] Gui-Song Xia, Julie Delon, and Yann Gousseau. Accurate junction detection and characterization in natural images. International Journal of Computer Vision, 106(1):31–56, Jan 2014. [21] Harry Pratt, Bryan M. Williams, Jae Yee Ku, Charles Vas, Emma McCann, Baidaa Al-Bander, Yitian Zhao, Frans Coenen, and Yalin Zheng. Automatic detection and distinction of retinal vessel bifurcations and crossings in colour fundus photography. Journal of Imaging, 4(1), 2018. [22] Sheng He, Marco Wiering, and Lambert Schomaker. Junction detection in handwritten documents and its application to writer identification. Pattern Recognition, 48(12):4036–4048, 2015. [23] M Elena Martinez-Perez, Alun D Hughes, Alice V Stanton, Simon A Thom, Neil Chapman, Anil A Bharath, and Kim H Parker. Retinal vascular tree morphology: a semi-automatic quantification. IEEE Trans Biomed Eng, 49(8):912–917, August 2002. [24] K. Liu, Y.S. Huang, and C.Y. Suen. Identification of fork points on the skeletons of handwritten chinese characters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(10):1095–1100, 1999. [25] Chungnan Lee and Bohom Wu. A chinese-character-stroke-extraction algorithm based on contour information. Pattern Recognition, 31(6):651– 663, 1998. [26] Michael Maire, Pablo Arbelaez, Charless Fowlkes, and Jitendra Malik. Using contours to detect and localize junctions in natural images. In 2008 IEEE Conference on Computer Vision and Pattern Recognition, pages 1–8, 2008. [27] Ran Su, Changming Sun, and Tuan D. Pham. Junction detection for linear structures based on hessian, correlation and shape information. Pattern Recognition, 45(10):3695–3706, 2012. [28] R. Deriche and T. Blaszka. Recovering and characterizing image features using an efficient model based approach. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 530–535, 1993. [29] He Zhao, Yun Sun, and Huiqi Li. Retinal vascular junction detection and classification via deep neural networks. Computer Methods and Programs in Biomedicine, 183:105096, 2020. [30] Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick. Mask R-CNN. CoRR, abs/1703.06870, 2017. [31] I B Puchalska, G A Jones, and H Jouve. A new aspect on the observation of domain structure in garnet epilayers. J. Phys. D: Appl. Phys., 11(15):L175, oct 1978. [32] Hae Yong Kim and Sidnei Alves de Araújo. Grayscale template-matching invariant to rotation, scale, translation, brightness and contrast. In Domingo Mery and Luis Rueda, editors, Advances in Image and Video Technology, pages 100–113, Berlin, Heidelberg, 2007. Springer Berlin Heidelberg. [33] J. P. Lewis. Fast normalized cross-correlation. Vision Interface, 95:120, 1995. [34] Sidnei Alves de Araújo and Hae Yong Kim. Ciratefi: An rst-invariant template matching with extension to color images. Integr. Comput.-Aided Eng., 18(1):75–90, jan 2011. [35] Hae Yong Kim. Rotation-discriminating template matching based on fourier coefficients of radial projections with robustness to scaling and partial occlusion. Pattern Recognition, 43(3):859–872, 2010. [36] Li Deng. The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Processing Magazine, 29(6):141–142, 2012. [37] Christian Eggert, Stephan Brehm, Anton Winschel, Dan Zecha, and Rainer Lienhart. A closer look: Small object detection in faster r-cnn. In 2017 IEEE International Conference on Multimedia and Expo (ICME), pages 421–426, 2017. [38] T. Yamada, Y. Suzuki, C. Mitsumata, K. Ono, T. Ueno, I. Obayashi, Y. Hiraoka, and M. Kotsugi. Visualization of topological defect in labyrinth magnetic domain by using persistent homology. Vacuum and Surface Science, 62(3):153–160, 3 2019. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

What Can Defects Tell Us About Magnetic Materials? TM-CNN's Defect Whisperer Approach

India is considered WhatsApp’s largest market, with many Indians using WhatsApp as their primary communication tool and hundreds of millions of monthly active users. Despite the app’s immense popularity in the market, it has faced significant regulatory challenges from the Indian government, with a Delhi High Court mandating traceability (resulting in the breaking of WhatsApp’s end-to-end encryption) in April 2024, followed by a five-year ban on sharing information collected from WhatsApp by India’s Competition Commission in November 2024. Despite these regulatory challenges, WhatsApp received approval from India’s National Payments Corporation of India (NPCI) in December 2024 to extend its UPI services through WhatsApp Pay to its entire Indian user base. The NPCI, which oversees the country's retail payments and settlement infrastructure, granted this permission, marking a significant expansion for WhatsApp Pay in its largest market. This ruling speaks to the importance of WhatsApp’s role in Indian digitalization but also highlights the increasing footprint, and therefore, a potentially Meta-branded super app, within India. The Indian digital payments landscape In 2016, the NPCI introduced the Unified Payments Interface (UPI), a real-time, instant payment system that facilitates both peer-to-peer and merchant transactions. UPI’s core strength in enabling intermediate funds transfers through a phone number or UPI ID, along with its ability to integrate with third-party applications, has made it the de-facto backbone of all digital payments in India. UPI is also playing a significant role in the implementation of India’s digital rupee central bank digital currency (CBDC), highlighting India’s appetite for digitalization. Popular e-payment applications in India today include Google Pay, Walmart-backed PhonePe, Paytm, CRED, and BharatPe, which all utilize UPI to facilitate transactions. Since their respective launches, all of these applications have integrated a variety of additional digital payment features, including offline transactions, utility bill payments, Before this decision, WhatsApp had been limited in onboarding UPI users for Whatsapp Pay, with an initial cap of 1 million users in 2020 (later raised to 100 million users in 2022), which required WhatsApp Pay to take a phased approach to expand its UPI user base. Therefore, despite currently having a relatively small share of the payments market, the removal of the user cap for WhatsApp will enable significant market share capture by India’s top communication application, with the merging of both communications and payments services potentially establishing WhatsApp’s, and a Meta-developed super app’s, dominance in the digital Indian market. Superapps in India Most popularized throughout the globe by WeChat and AliPay, superapps have played a big role in the digitization of Southeast Asian countries, with Singapore’s Grab, Indonesia’s GoJek, and South Korea’s KakaoTalk revolutionizing everything from ride-hailing to food delivery to digital payments, among many other services. Despite these positive trends elsewhere throughout the world, superapps in India have not received much success, with one prime case study being Tata Group’s Tata Neu, launched in 2022, which has experienced exponentially declining interest due to many different problems. This failure could be due to market saturation from entrenched players, lack of interest from local audiences, high market share capture costs, and/or a myriad of other factors. However, the removal of barriers against WhatsApp, combined with its existing popularity, especially when combined with capabilities offered by other Meta products, could help supercharge a Meta-built platform to superapp status in India. What’s next for WhatsApp (and Meta)? The Indian social media market is already dominated by Meta products such as Facebook and WhatsApp, with Indians being the primary driver behind Meta-owned platform downloads. As a result, Meta’s foothold in India remains strong, giving rise to the potential of a Meta-built application with WhatsApp integration being India’s first undisputed superapp. Meta is deeply entrenched in India in both private and public contexts. On the private front, Meta announced an expansion of its business subscription program to Indian users in July 2024, followed by an October 2024 announcement of a Meta partnership focusing on generative artificial intelligence (AI) with global conglomerate Infosys. One recent public sector partnership included the September 2024 announcement of Meta to work with the Telangana government to provide Indians with emerging technologies such as AI to enhance e-governance and citizen services. Another recent public partnership included the October 2024 announcement to partner with India’s Ministry of Skill Development and Entrepreneurship (MSDE) to integrate AI and VR into Skill India to increase and incubate talent Indian talent. Conclusion While an independently-created Meta-developed superapp, or even enhancements on WhatsApp, may seem far-fetched to some, the fact remains that Meta controls India’s most popular communication and social media platforms. The removal of restrictions on onboarding users to digital payments for one of those platforms, WhatsApp, highlights an immense opportunity for Meta to develop India’s first widely-adopted super-app. However, regardless of whether a Meta-built superapp is in development or not, it is clear that WhatsApp has and will continue to play an important role in Indian digitalization, whether through its status as India’s #1 communication platform or as a growing digital payments application. Image source: PYMNTS.com India is considered WhatsApp ’s largest market , with many Indians using WhatsApp as their primary communication tool and hundreds of millions of monthly active users. Despite the app’s immense popularity in the market, it has faced significant regulatory challenges from the Indian government, with a Delhi High Court mandating traceability (resulting in the breaking of WhatsApp’s end-to-end encryption) in April 2024 , followed by a five-year ban on sharing information collected from WhatsApp by India’s Competition Commission in November 2024 . WhatsApp largest market largest market primary communication tool primary communication tool April 2024 April 2024 November 2024 November 2024 Despite these regulatory challenges, WhatsApp received approval from India’s National Payments Corporation of India (NPCI) in December 2024 to extend its UPI services through WhatsApp Pay to its entire Indian user base. The NPCI, which oversees the country's retail payments and settlement infrastructure, granted this permission, marking a significant expansion for WhatsApp Pay in its largest market. received approval This ruling speaks to the importance of WhatsApp’s role in Indian digitalization but also highlights the increasing footprint, and therefore, a potentially Meta-branded super app, within India. The Indian digital payments landscape In 2016 , the NPCI introduced the Unified Payments Interface (UPI), a real-time, instant payment system that facilitates both peer-to-peer and merchant transactions. UPI’s core strength in enabling intermediate funds transfers through a phone number or UPI ID, along with its ability to integrate with third-party applications, has made it the de-facto backbone of all digital payments in India. UPI is also playing a significant role in the implementation of India’s digital rupee central bank digital currency (CBDC), highlighting India’s appetite for digitalization. 2016 2016 significant role Popular e-payment applications in India today include Google Pay, Walmart-backed PhonePe, Paytm, CRED, and BharatPe, which all utilize UPI to facilitate transactions. Since their respective launches, all of these applications have integrated a variety of additional digital payment features, including offline transactions, utility bill payments, include include Before this decision, WhatsApp had been limited in onboarding UPI users for Whatsapp Pay, with an initial cap of 1 million users in 2020 (later raised to 100 million users in 2022 ), which required WhatsApp Pay to take a phased approach to expand its UPI user base. 2020 2020 2022 2022 Therefore, despite currently having a relatively small share of the payments market, the removal of the user cap for WhatsApp will enable significant market share capture by India’s top communication application, with the merging of both communications and payments services potentially establishing WhatsApp’s, and a Meta-developed super app’s, dominance in the digital Indian market. Superapps in India Most popularized throughout the globe by WeChat and AliPay, superapps have played a big role in the digitization of Southeast Asian countries, with Singapore’s Grab, Indonesia’s GoJek, and South Korea’s KakaoTalk revolutionizing everything from ride-hailing to food delivery to digital payments, among many other services. Most popularized Most popularized superapps superapps Despite these positive trends elsewhere throughout the world, superapps in India have not received much success, with one prime case study being Tata Group’s Tata Neu, launched in 2022 , which has experienced exponentially declining interest due to many different problems . This failure could be due to market saturation from entrenched players, lack of interest from local audiences, high market share capture costs, and/or a myriad of other factors. 2022 2022 different problems different problems However, the removal of barriers against WhatsApp, combined with its existing popularity, especially when combined with capabilities offered by other Meta products, could help supercharge a Meta-built platform to superapp status in India. What’s next for WhatsApp (and Meta)? The Indian social media market is already dominated by Meta products such as Facebook and WhatsApp, with Indians being the primary driver behind Meta-owned platform downloads. As a result, Meta’s foothold in India remains strong, giving rise to the potential of a Meta-built application with WhatsApp integration being India’s first undisputed superapp. dominated dominated Meta primary driver primary driver Meta is deeply entrenched in India in both private and public contexts. On the private front, Meta announced an expansion of its business subscription program to Indian users in July 2024 , followed by an October 2024 announcement of a Meta partnership focusing on generative artificial intelligence (AI) with global conglomerate Infosys. One recent public sector partnership included the September 2024 announcement of Meta to work with the Telangana government to provide Indians with emerging technologies such as AI to enhance e-governance and citizen services. Another recent public partnership included the October 2024 announcement to partner with India’s Ministry of Skill Development and Entrepreneurship (MSDE) to integrate AI and VR into Skill India to increase and incubate talent Indian talent. July 2024 July 2024 October 2024 October 2024 September 2024 September 2024 October 2024 October 2024 Conclusion While an independently-created Meta-developed superapp, or even enhancements on WhatsApp, may seem far-fetched to some, the fact remains that Meta controls India’s most popular communication and social media platforms. The removal of restrictions on onboarding users to digital payments for one of those platforms, WhatsApp, highlights an immense opportunity for Meta to develop India’s first widely-adopted super-app. However, regardless of whether a Meta-built superapp is in development or not, it is clear that WhatsApp has and will continue to play an important role in Indian digitalization, whether through its status as India’s #1 communication platform or as a growing digital payments application. Image source: PYMNTS.com Image source: PYMNTS.com PYMNTS.com

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