Category: Data Security

Overcoming Barriers to Generative AI in Life Sciences R&D

November 13, 2024

In the realm of life sciences research and development (R&D), generative AI holds transformative potential, accelerating advancements in drug discovery and optimising clinical trials. Yet, data privacy and regulatory compliance present significant barriers to its widespread adoption. Navigating these complexities is crucial for life sciences organisations to harness AI’s power while safeguarding sensitive data and adhering to stringent regulations. The Importance of Data Privacy in Life Sciences Generative AI models rely on extensive datasets to predict molecular structures, generate drug candidates, and simulate patient responses. Much of this data is inherently sensitive, involving personal health information (PHI), genetic data, and proprietary research findings. Beyond being a legal requirement, ensuring data privacy is a moral obligation, governed by regulations like the General Data Protection Regulation (GDPR) in the European Union. Breaching these laws risks severe penalties, loss of public trust, and possible litigation. Therefore, R&D teams must implement rigorous data anonymisation, encryption, and access control protocols when employing generative AI. Balancing Data Access with Compliance One major challenge in leveraging generative AI is achieving a balance between data accessibility and regulatory compliance. Effective model training often requires data sharing across multiple research teams and jurisdictions, each with its own regulations. To tackle this, life sciences organisations can turn to federated learning, allowing AI models to train across decentralised data sources without relocating the data. This approach maintains data privacy, as only model updates—not raw data—are shared, reducing the risk of breaches. Implementing Advanced Data Security Measures Standard practices like data anonymisation and encryption may fall short under the rigorous demands of compliance frameworks. Life sciences R&D firms should adopt advanced security measures, such as homomorphic encryption and differential privacy. Homomorphic encryption enables computations on encrypted data, keeping it secure during processing, while differential privacy adds mathematical noise to datasets to prevent tracing individual data points back to specific persons. Combining these methods with robust access protocols, blockchain for data traceability, and regular audits helps organisations protect both the organisation and the individuals whose data they use. Navigating Regulatory Complexities Different countries interpret sensitive data differently, complicating global research efforts. For instance, GDPR emphasises individual rights over personal data, while other regions may focus on varying aspects of data security. To manage this, life sciences companies should establish compliance management systems that adapt to changing laws and standards. A dedicated compliance team can help monitor AI processes to ensure they align with diverse global standards. Building Stakeholder Trust Transparency is vital to gaining the trust of stakeholders, including patients, healthcare providers, and regulators. Life sciences companies can foster this trust by implementing explainable AI (XAI) techniques, which reveal insights into generative models’ decision-making. Regular communication on data management practices and adherence to ethical standards reinforces credibility and promotes collaborative research. Conclusion The life sciences industry is poised for transformation with the integration of generative AI in R&D. However, addressing data privacy and compliance challenges is essential to unlocking its full potential. By adopting advanced security measures, leveraging federated learning, and maintaining regulatory compliance, organisations can drive innovation while protecting sensitive data and sustaining public trust. Implementing generative AI in life sciences requires a balanced approach that respects data privacy without stifling progress, paving the way for groundbreaking advancements. FAQs 1. What impact does generative AI have on life sciences R&D? Generative AI is revolutionising life sciences by accelerating drug discovery, optimising clinical trials, and simulating patient outcomes. This technology helps researchers explore molecular structures, identify potential drug candidates faster, and bring innovative treatments to market more efficiently. 2. Why is data privacy essential in AI-driven life sciences research? Generative AI relies on vast datasets, often including sensitive information like personal health data and proprietary research. Protecting this data is both a legal and ethical responsibility, crucial for complying with regulations like GDPR and maintaining public trust in research institutions. 3. How do life sciences organisations ensure data privacy while using AI? By adopting federated learning, life sciences teams can train AI models on decentralised datasets without moving data across jurisdictions. This method allows for privacy preservation and compliance while enabling cross-border collaboration and innovative research. 4. What advanced security measures are used to protect sensitive data? Life sciences R&D benefits from advanced techniques like homomorphic encryption, allowing computations on encrypted data, and differential privacy, which obscures individual data points. Blockchain for traceability and regular security audits further strengthen data protection and compliance. 5. How can companies build trust with stakeholders while using generative AI? Transparency is key. Life sciences organisations build trust by using explainable AI (XAI) methods that clarify how AI models make decisions. Open communication about data practices and ethical standards reassures stakeholders, supporting collaborative and ethical AI-driven research.

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Debunking Myths

Debunking Myths: Cloud Computing vs. Data Security

August 20, 2024

There is a raft of myths pertaining to data security in the cloud that need to be combated, particularly for those still doubting the relevance and importance of cloud-based solutions. Data access, management, and saving has been revolutionized fully with the growth of cloud computing. However, there are several misconceptions which are linked to the security of cloud data. Here’s looking at some of these myths that should be debunked below. Key Myths Regarding Data Security in the Cloud Let us take a closer look at myths and misconceptions pertaining to secure cloud storage below. Shared Responsibility Models- Key Misconceptions There are several myths relating to shared responsibility models and the potential for security breaches. Some of them include the following: In the shared responsibility model, cloud providers are responsible for various aspects. They have invested considerably in scaling up security levels, which encompass the physical security offered by data centers and also network security. They usually offer certifications for diverse industry regulations too, including PCI DSS, HIPAA, and GDPR. Many companies also end up assuming that providers will configure environments to be more secure as a default service, although this cannot be expected at all times. Misconfigurations are often an issue that leads to security problems. Companies should not only put in a proper tracking system as a part of their shared responsibility, while also understanding that just moving to the cloud does not make them compliant entities. How It Stacks Up For Companies Data security in the cloud has several myths and wrongful perceptions around it, which have been busted above. At the same time, the shared responsibility concept has to be taken seriously by companies as well. There is an element of continual tracking and knowledge regarding regulations and compliance that organizations cannot wish away. Sometimes, cloud security seems improbable due to the lack of understanding among companies in relation to security risks linked to cloud ecosystems. Many a time, companies do not allocate proper resources towards cloud security as well on the grounds of convenience. Hence, this may lead to the absence of suitable security controls and higher vulnerability. The shared responsibility model has to be fully understood, where companies should know the areas of cloud security that they should invest more in and also how to build security blueprints likewise. While providers hold responsibility for securing cloud infrastructure, inclusive of the physical security of data centers, virtualization layer, and network security, companies hold responsibility for the security of operating systems, apps, and other data that run on the cloud-based infrastructure. Hence, while cloud environments are secure enough at a tertiary level, organizations also have to usher in necessary policy and technological changes to make it work. To sum it up, cloud providers hold responsibility for securing the cloud itself, while the companies have the responsibility to secure their data within the cloud. FAQs 1. Isn’t cloud storage inherently less secure than keeping my data on-premise? Cloud storage is not intrinsically less secure in comparison to keeping data on-premises. This is because providers offer similar security levels while having implemented multiple measures and upholding stringent regulations related to data security. 2. Can cloud providers access my data without permission? It is not that easy for cloud providers to access your data without authorization. There are limitations and data security regulations that bind them in this regard. 3. What happens to my data if the cloud provider experiences a security breach? Data may be compromised in case of a security breach at the cloud provider’s end. However, companies hold responsibility to secure their own data within the cloud to prevent any adverse impact. 4. Does cloud computing offer any security advantages over traditional data storage? Cloud computing may offer more advanced security in comparison to conventional data storage, since cloud providers help companies comply with changing regulations, while infrastructure and processes are also regularly updated. 5. How can I ensure my data remains secure when using cloud computing services? Some of the ways to ensure data security in the cloud include not storing highly sensitive or confidential data there, reading user agreements to find out the cloud service storage functions, making passwords in an informed manner, encryption measures, and also using an encryption cloud service.

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Account Aggregator Framework

Account Aggregator Framework: Overview, Uses, And More

December 14, 2022

The RBI’s account aggregator framework has been a veritable revolution, considering its applicability and several other factors. Data is now the lifeblood for everything in the modern world. This is where the discussion around account aggregator frameworks and their types begins.  What Is An Account Aggregator Framework?  The RBI came up with the account aggregator framework in 2021 for ensuring the easier accessibility of financial information through various data-based intermediaries or account aggregators in India. These are the intermediaries, as guided by the account aggregator framework RBI, who have responsibility for the collection of financial details of users from multiple entities like FIPs (financial information providers) who hold data of consumers, and share it with those entities seeking data on the basis of the consent of the user.  The framework enables end-users to obtain major control over financial information, being a major move towards helping users benefit from their data usage with multiple advantages. It will ensure a more efficient method of financial information sharing, helping lower costs of transactions and financial fraud related risks. This will help users also access diverse banking solutions at lower costs and quickly at that. While there can be account aggregator framework types available, the process involves compiling financial data from various sources. This includes income, invoices, expenditure, equity related investments, deposits, receipts, tax returns, and a lot more.  The AA mechanism has ample scope towards data democratization and filling up the gaps between users and FIPs.  How To Use The Service Of Account Aggregators?  According to several reports, the AA framework is aiming at developing on trust as delivered by credit bureaus, towards offering an extensive global data stack for all lenders. Better financial education and the AA mechanism will eventually help those consumers in understanding the impact of their activities. The account aggregators also help in ensuring improved credit for underserved/un-served individuals. Most financial institutions adhere to a pre-defined system for ensuring credit availability for borrowers. This encompasses identity verification, regulation, assessment of credit risks, and more. They take into account credit history of borrowers and it is often difficult for borrowers without financial history to get credit. The framework will help in building repayment abilities and trustworthiness of these borrowers via data sources which are well-defined for creating the trust of the lender.  The procedure by which NBFCs and banks ensure available credit for borrowers is based on multiple aspects, right from verifying identity to regulation to also assessing credit risks. With the high population of underserved customers still in the market, and the increasing spread of digital technologies and payment systems across the country, the importance of AA frameworks and solutions will heighten even more.  The Future Of AA Framework The AA framework will ultimately ease the entire procedure of assessing credit risks, while making it accessible and reasonably priced as well. With more users tapping into the ecosystem, there will be newer use cases coming up for consumers. MSMEs and individuals will equally benefit from such frameworks. FIUs or financial information users also expect that AA will be ensuring these objectives, while making the procedure of assessment more affordable and also timely. There could be benefits via the digital framework, a consumer-friendly dashboard, data sharing with the approval of users, and easier controls.  With users coming on board this AA system, there will be services that will come up in the future. MSMEs seeking business loans expansions or working capital will naturally profit from their account or payment based relationships, while getting exposure to competing financial service solutions who are striving to attract their business.

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For Enterprises, what does it mean to be AI ready?

February 28, 2020

It would be so cool if we can ask Amazon what shoes should I buy, ask Siri about the cause of the delay in my cab or even request Google AI to fix my fuse. AI is expected to dramatically reshape fundamental business processes that serve faithfully in the background, enabling digitization to fully penetrate key business interactions and transactions. Why do enterprises need AI? Businesses today need to run at the same pace to stay in this competitive market while balancing the wavering factors like knowledge retention, sustainability, scalability, etc. To achieve the pace and agility, an enterprise requires exemplary harmony, self-governing data, content and strong management in such a way that it provides meaningful support to all the business problems. As the volume of this data keeps increasing exponentially, it becomes a little difficult to derive valuable insights from them. As a result, it affects the decision-making process. AI is helping in creating the maximum opportunities by being a great business driver. A survey conducted by BCG (Boston Consulting Group) among 112 CIOs across multiple industries saw that Artificial Intelligence technologies could significantly improve the cost-effectiveness and performance of IT operations, allowing organizations to stimulate innovation rapidly without making any sacrifice on service, security, or stability. Where does the setback originate? AI has proved itself to be revolutionary but if we look at the other side, it leads to certain setbacks too. Data crisis can lead to hazardous errors Development and training go hand in hand. When you develop an AI-based product, it needs to be supported by both monetary and non-monetary factors like training. A recent failure tasted by IBM purely justifies the statement. In 2013, IBM partnered with The University of Texas MD Anderson Cancer Center and developed a new health care system called “Oncology Expert Advisor” with a mission to eradicate cancer. In July 2018, it was found that the AI was recommending erroneous treatment solutions. The technical experts suggested that the main reason behind it was the lack of training on real cancer patients. Too fragile to respond to the supporting data There are several instances where the outcome of AI and machine learning proves to be biased, sexist and misogynistic. There cannot be a better case study than Amazon’s AI for recruitment to justify the statement. Amazon has developed an AI that shortlists the best 5 candidates out of the given 100 resumes. They trained their AI based on current engineering employees who were male and white. Therefore AI learned that white men are the perfect fit for engineering jobs. Lacks a mind of its own AI is artificially sensed, as it does not have a brain of its own. It was witnessed in the case of our commuter partner Uber. Uber’s self-driving car was running at a speed of 61kmph and could not recognize the lady who appeared from nowhere in the dark, resulting in a crash. In the previous couple of years, enterprises have seen many cases of AI failures. The tiniest of loopholes can lead to big problems. The product managers need to spend the maximum time testing the product. Can AI still be game-changing? There are two areas of artificial intelligence that are most applicable, such as Machine learning and Natural Language Processing. Machine learning is a subset of AI techniques that uses statistical methods to enable machines to improve with experiences. Whereas Natural Language Processing is a subsidiary technology of AI that understands and responds to everyday conversational language such as Alexa, Google assistant, chatbot. These artistic features are world-changing algorithms. According to a report, It is claimed that, by 2035, Artificial Intelligence will have the power to increase productivity by 40% or more. Enterprises who have explored the core use of artificial intelligence have reached a long way. There are a few examples that satisfy the statement pretty well. Dominate the global business The e-commerce giant Alibaba used Artificial intelligence and machine learning to expand its business operations all over the world. They collect the data related to the purchasing habits of the customers. With natural language processing, it automatically generates product descriptions for the site. It has also used AI algorithms to reduce traffic jams by monitoring every single vehicle in the city. Additionally, with the help of its cloud computing division called Alibaba Cloud, it is helping the farmers to monitor crops to improve yield cost-effectively. China is planning to be a dominant AI player and build an AI industry worth $1 trillion by 2030. Back up huge profits An AI software company, Sidetrade, has built a core AI platform known as AIMIE (Artificial Intelligence Mastering Intercompany Exchanges) that processes 230 million B2B transactions. That’s equal to around $700 billion sales and finance undertakings over the last three years. Web-crawling robots further enrich this data with 50 billion data points collected through websites, social networks, and online media sources that are relevant to the activity of 23 million European companies. What AI can do for your enterprise? If a business looks at Artificial intelligence with the lense of business capabilities, it will wipe off 50% off their pressure. AI majorly contributes to three major areas in business: Business process automation, Customer engagement, and Insight development through data analysis. AI can help an organization in creating new products, enhancing its features, providing a creative workspace to the employees by automating tasks, making better decisions, optimizing market operations, etc. In a survey conducted among 250 employees of a particular organization, the percentage of the contribution made by AI in an organization was found. Is your Organisation AI ready? Every enterprise needs to analyze if they are ready for the AI revolution. To bring AI into the business mainstream, companies need to complement their technology advances with a focus on governance that drives ethics and trust. If they fail to do so, their AI efforts will fall short of expectations and lag the business results delivered by competitors that responsibly embrace machine intelligence. Organization gotta embrace the

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Why Cloud Based Innovation Is the New Norm for Insurers Globally?

September 9, 2019

A number of insurers have started migrating their core business functions into the cloud, a sight which was rare a few years ago. A report by Ovum a few years ago had highlighted how 67% of CIOs from the insurance industry predicted that cloud computing will “completely transform the insurance industry in five years or less”. Today, we are excited to see that it is happening. But the bigger question is why is it happening? The reasons for adoption enlisted at a strategic level include enhancing operational efficiencies, adapting to agility and seamless access to disruptive technology that can drive innovation. Let us see a simple use case of FinanceFox AG, they have teamed up with Salesforce to integrate CRM applications with its insurance brokerage platform.  The platform lets users manage all exiting insurance policies and get advice on insurance coverage gaps. This has given users complete control and the organization is acting as a virtual advisor 24×7. Cloud-based insurance solutions have ushered in an era of innovation to such an extent that large enterprises are craving for a pie in this segment already. For instance, Alibaba Cloud had partnered with eBaoTech to launch ‘eBaoCloud’, an insurance cloud platform that grants insurers access to standardized insurance capabilities without the need to self-build or deploy their own systems. Recently, China Continent Insurance (CCIC) launched its next-generation Core System based on ‘eBaoCloud InsureMO’ as middleware. ‘InsureMO’ provides a comprehensive set of APIs to develop an ecosystem for connecting all internal staff user interfaces and workflows. If we look around we see a dynamic range of companies that are leading the race with cloud-based innovations. Source: Accenture Though we assumed that cloud solutions are only for boosting operational efficiencies and agility but they are evolving themselves to play a crucial role in combating insurance fraud that has plagued the industry for a long time.  For example, CNA Financial Corporation uses Shift Technology’s ‘FORCE’ fraud detection solution to automate the insurer’s fraud detection capabilities. ‘FORCE’ is a SaaS-based solution and claims to have a 75% hit rate. CNA Financial is charged based on the volume of claims processed. Did you know? According to Insurance Thought Leadership.com, insurers across US and Europe fall prey to insurance frauds worth approximately Euro 60 bn per annum. While an estimated 65% of fraudulent claims go undetected, about Euro 240 mn is spent by insurers to tackle fraud. Journey to Being Cloud-Native By now you must have already started planning on leveraging cloud for growth. Let us help you make the first move. An insurance company which provides motor insurance plans to decided of creating a smarter claims prevention mechanism and move it to the cloud. They would then be engaged in a number of considerations and scenarios like: The application and infrastructure model should be designed to account for unpredictable data flow The system must be able to decipher possible business implications from a weather alert of a possible thunderstorm It should have the necessary ability and algorithms to prioritize sending of push notifications and warning messages to policyholders while not hindering other workloads. While they check and analyse the requirements and possibilities they have to keep the core principles intact. But what are these core principles? Clear identification of all possible infrastructure requirements, architectural patterns and application models Well established difference between infrastructure resources that required smart configuration, and provisioning in case of an unforeseen scenario Drafting of architecture patterns for achieving scalability and flexibility of services to cater to the surge in data flows Finalization of application model to optimize solutions for both low configuration and scale-out scenarios Figure: Guardian Life’s shift to AWS Now we feel like we’re in a platform. And we’re capable of testing and learning a lot faster than we have in the past with much less of an investment. But we also have access to these new technologies and just as importantly, the people that are developing these new technologies.”- Dean Del Vecchio, EVP, CIO and Chief Of Operations at Guardian LifeThe partnership between insurers and cloud is here as to stay with cost advantages, co-creation possibilities and higher sustainability. With major giants already deciding to get out of the business of owning and operating its own data centres is a huge milestone for the new cloud-first approach.

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KYC

Centralized KYC System: Yet a dream in India!

June 13, 2019

The centralised KYC, designed to reduce the burden of producing KYC documents and getting verified every time when the customer creates a new relationship with a financial entity; has been under the scanner due to its high costs and hybrid model of physical + digital verification . Additionally, data protection concerns of the banks and other institutes has never allowed it to take the growth curve, it was expected to. However, the advantages offered by the centralised database can never be denied.With CKYCR a number of risks can get mitigated as unlike Aadhaar (which offered similar KYC functionality), the CKYCR does not include biometric information, which reduces potential data protection risks. What needs to be acknowledged here is that CKYCR was meant to be interoperable without sensitive personal information sharing. It falls under the rules of data protection framed under the Information Technology Act and the proposed data protection law provided that data collected from a customer can only be used for the purpose to which the customer consented to. Nonetheless, large banks remain wary about making the public database accessible since they will be the biggest contributor of data and are also fixated on security concerns and misuse of data by small FinTech’s or regulators –that can threaten the reputation of the bank providing the data. While the industry players/FinTechs are still juggling with different options as an alternative of paper-based KYC verification; a number of brands are banking on these innovations. Tata Mutual Fund has launched “video KYC” as a digital solution to KYC verification. Evidently, video solution comes as one of the most secure, efficient and accurate form of verification that caters to most of the concerns stated above. Open banking is already in use as a collaborative model in which banking data is shared through APIs between two or more unaffiliated parties. APIs have been used for decades, particularly in the United States, to enable personal financial management software, to present billing detail at bank websites, and to connect developers to payments networks like Visa and Mastercard. To date, in India however, these connections have been used primarily to share information rather than to transfer money. Given the little justification for repeating the same KYC procedure across different financial products and the time and cost this entails –dedicated API as developed by NCPI could instead be used for the benefit of investors. Answer comes in the form of DigiLocker as well. It can leverage synergies towards a better KYC and can also be used beyond identity-related documents. Many banks have already started using it for various purposes, including reviewing documents for loan applications. Recently, ICICI Bank has integrated its retail internet banking platform with DigiLocker. In a recent event at MCCI Fintech Forum 2019, Shri Deepak Kumar, CGM-In-Charge, Department of Information Technology, Reserve Bank of India said, “As a technologist, I have no doubt that options beyond Aadhaar based KYC has any limitations in execution. However, compliance issues still remains a key challenge. The sooner it gets solved, the better it is”. As banks continue to refrain from sharing KYC data in the absence of mutual benefits the Government has allowed non-banking firms to verify customer data through offline Aadhaar verification, which can be done by the use of KYC XML or QR code. Though the process is still evolving, the year 2019 can be a deciding one. Keep your eyes open!

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Why API Integration Is A Must For Digital Banking Growth In 2019

May 30, 2019

The banking industry is currently overwhelmed by technological disruptions and heightened customer expectations, with non-traditional players such as Facebook, PayPal, Google, and others quickly usurping roles previously played by banks. Non-traditional players have access to cutting-edge technology, which results in excellent user experience (UX) and innovative financial solutions. Customer expectations cannot be met by traditional banks which restrict themselves to digital solutions such as mobile apps or 24/7 customer service. However, banks can choose to be savvy and make the right choice of opening up their APIs to these third-party products and applications. According to one survey, 55% of financial institutions believe that API integration is critical to business strategy. Banks need to collaborate with newer and non-traditional players and open up their APIs in order to remain competitive and witness growth. API integration is an urgent need Behavioral changes and customer preferences have vastly changed over the years, with millennials and Generation Z expecting more from their banks than older users. Providing excellent customer service and a great mobile application are simply not enough anymore, because of the innovative disruptions initiated by non-traditional players. According to a report published by Intelligent Finance, Baby Boomers (or those born between 1946 and 1964) considered poor face-to-face customer service as a major determinant to exit a bank, while millennials revealed they would exit a bank not only if they disliked its smartphone app but also if it suffered from security breaches. Younger customers are also likely to quit a bank if they are unable to use their bank accounts on third-party applications and products. This is a gap that non-traditional players have capitalized on, and is an existential threat to traditional banks. People aged between 18 and 34 are two times likelier than older customers to use mobile payments and P2P lending products. In addition, the same demographic group prefers to receive constant updates via preferred channels such as text message, app notifications, etc. As Millennials grow older and more affluent, and as Generation Z takes the place of the millennials, the importance of digital banks providing a holistic financial ecosystem consisting of third party products and services used by customers become more apparent. Here are some successful examples of API integration: People with financial difficulties in the USA have started to use P2P lending tools such as Earnin and PayActive. It is now possible to consolidate debt too, thanks to debt aggregators. Marcus from Goldman Sachs and SoFi are often cited as examples for non-traditional lenders. Often, these tools are integrated with e-commerce sites or food delivery apps so that people can purchase what they need on credit, bypassing banking lending rules. Credit unions are a non-traditional alternative to bank loans. Walmart MoneyCenters are extremely popular today because they offer a borrowing alternative to people with poor credit histories. If banks integrate their data with these products, customers can continue to make payments for P2P loans without canceling their accounts. One of the best examples of API integration is when PayPal decided to integrate its API with Siri. iPhone users can send and receive money via PayPal by speaking to Siri. Wave is an invoicing and accounting software used by businesses and individuals. Wave uses banking APIs to help users control all their business finances in a single place. It collects as much data as possible from various sources and even markets loans provided by OnDeck on its platform to eligible users. Larger banks have started to offer data aggregation services to their customers. For instance, HSBC recently launched its Connected Money app, on which customers can view their account details in 21 other banks without ever leaving HSBC’s application. Facebook Messenger payments allow Facebook users to transfer money to their friends without ever having to leave the network. Facebook currently has integrated the APIs of PayPal, Stripe, Visa, MasterCard, American Express and others. If traditional banks do not understand the metamorphosis that has already taken place, they stand to lose more of their existing and future customers to non-traditional players. Specific reasons for API integration In order to survive technological disruption, banks need to engage in business model reinvention, which includes open banking and partnering with the newer third-party apps and products. While the producer market consists of banks and other financial enterprises that create products and services, customers can access these products and services on third-party applications, websites and or use voice control. Capitalizing on these distribution channels by opening up APIs is very important for banks survival. E-commerce and on-demand services such as Uber and Airbnb have spurred a customer-centric demand for always-on banking Internet of Things (IoT) enabled devices have led to a growing need for smart solutions and banking services available on intelligent devices Omni-channel banking experience requires data exchange between apps, and customers take this facility for granted now What kind of apps need integration? New services and applications that need API integrations with banking applications include: Payments, clearing and settlement services Mobile and web-based payment applications Digital currencies (DCs), Blockchain and Distributed Ledgers Deposits, lending, and capital raising services Crowdfunding Market provisioning services such as smart contracts and e-aggregators Emerging technologies such as Big Data, cloud computing, Artificial Intelligence (AI) and robotics (Robo-advice) Electronic trading and insurance API Integration can prove to be challenging If you thought your in-house developers can release an API along with the application, you will be disappointed to learn that in 2019, it is a very complex situation. Developing an integration workflow consumes the most amount of time during API Integration, and requires special skills. Event Driven Integration is far more complex than simple API integration, as it needs to provide real-time status updates to customers. Real-time status updates are crucial in today’s financial market. APIs are not always uniform and there are no industry standards at the moment. 70% of the developers work with REST APIs, which makes it a wise choice for API Integration. However, REST APIs will not work for all kinds of applications and

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