Choose Your Cloud Partner Wisely

Gartner Says By 2020, a Corporate “No-Cloud” Policy Will Be as Rare as a “No-Internet” Policy Is Today.

With the increasing number of cloud adoption rate, it has become instrumental for organizations to build a robust Cloud migrations strategy.

As per the Commvault report. the cloud Fear of Missing Out (FOMO) is driving the business leaders to move full speed ahead towards the cloud.

Many organizations are already moving part of their applications to cloud or planning to move all of their applications to cloud.  Apart from the reliability, scalability, cost optimization and security benefits, the recent disruption in cognitive technologies like AI/ML/Blockchain are one of the driving factors to embrace the cloud as an important IT strategy Most of the cloud providers are offering attractive easy to implement AI/ML platform along with other multidimensional benefits.

However, there are so many cloud providers, so many cloud services are available in the market.

Who is the best service provider? Which service model should be fit for the organizations?

The answers are not a single word or list of words. This is a process appropriately designed towards your business goals.

Hence choose a Cloud service provider who can work as a partner, not as a vendor……

This is a journey through the learning curve for both partners.

I am highlighting a few aspects which must be considered when selecting a cloud partner:

Define your migration strategy – IaaS vs PaaS vs SaaS. You need to select the right partner for platform, infrastructure and application services. Sometimes you may need to work with multiple providers for different services or you can have one combine managed Service partner.

The above diagram from Gartner is showing a perfect ownership sharing in various cloud service model. Like if you have a best in class application service team, you can procure infrastructure services or platform services from a service provider and align the internal team to run the cloud service. This will need extensive cloud training for the existing team, hire some cloud experts to build the in-house capability and robust service management process to coordinate among different vendors. Your cloud partner should take a role of your training partner in such cases. In a SaaS-based model, this is essential that cloud partner know your business and industry well. Because ultimately the cloud service will be fully integrated with the business model. Hence it is very important that the SaaS provider is fully aligned with your business need. Overall Selection Criteria can be designed by analyzing and comparing the below factors- 

The provider must be knowledgeable about your application, data, interfaces, compliance, security, BCP/DR and other business requirements. Critical Success Factors are defined in the various model. However, as per our study, we have listed Seven key success factors for cloud computing – Cloud Partner – The most important step towards success. Choose a perfect cloud partner who will help in your journey towards success. Cloud Strategy –

  • Create a plan & solution architecture
  • Define the cloud applications and services
  • Prepare the service catalogue
  • Build the capability and processes

Cost & performance – one of the most important success criteria.

  • Plan cost and ROI
  • Benchmark the performance
  • Proactive monitoring
  • Capacity planning
  • Right-sizing & optimization

Security –

  • Build the security strategy – secure all the layers and components
  • Automation, tooling and proactive monitoring
  • Plan the audit, compliance reporting & certification

Contract & SLA –

  • Incorporate all the aspects of the contract carefully with the legal help
  • Build customer and suppliers terms properly
  • Define Service SLA & service credits
  • Manage the contract (an ongoing process)

Automation –

  • Have an automation strategy
  • From Infrastructure to Application – automate the repetitive work
  • Increase the response and resolution
  • Reduce the human error

Manage the stakeholders –

  • Cloud adoption changing the organizational structure and IT landscape drastically.
  • Manage your stakeholders throughout the journey.
  • Assess the impact of positive and negative stakeholders on the project.

A managed service provider is the ideal solution in today’s complex world. At MagicFinServ, we are helping the global FinTech companies to build their successful SaaS model. Our highly skilled cloud team can align all the moving parts from architecting to implementation and deliver a production-ready solution. To know more about our FinTech focused cloud solution please contact us at www.magicfinserv.com.

5 ways in which Machine Learning can impact FinTech

Machine learning is one amongst those technologies that is invariably around us and that we might not even comprehend it. For instance, machine learning is employed to resolve issues like deciding if an email that we got is a spam or a genuine one, how cars can drive on their own, and what product someone is likely to purchase. Every day we tend to see these sorts of machine learning solutions in action. Machine learning is when we get a mail and automatically/mechanically scanned and marked for spam within the spam folder. For the past few years, Google, Tesla, and others have been building self-drive systems that may soon augment or replace the human driver. And data information giants like Google and Amazon can use your search history to predict which things you are looking to shop for and ensure you see ads for those things on each webpage you visit. All this useful and sometimes annoying behavior is the result of artificial intelligence.

This definition brings up the key component of machine realizing specifically, that the framework figures out how to tackle the issue from illustration information, instead of us composing a particular rationale. This is a noteworthy advancement from how most writing of computer programs is done. In more customary programming we deliberately examine the issue and compose code.

This code peruses in information and utilizes its predefined rationale to distinguish the right parts to execute, which at that point creates the right outcome.

Machine Learning and Conventional Programming

With conventional programming, we use code structs like– if statements, switch-case statements, and control loops implemented with — while, for and do statements. Every one of these announcements has tests that must be characterized. And the dynamic information, typical of machine learning issues can make defining these tests very troublesome. In contradiction to machine learning, we do not write this logic that produces the results. Instead, we gather the information we need and modify its format into a form which machine learning can use. We then pass this data to an algorithm. The algorithmic program analyses the data and creates a model that implements the solution to solve the problem based on the information and data.

Machine Learning: High-Level View

At a high level, machine learning could be understood in a way as shown in the following diagram:

Machine learning in fintech

We initially start with lots of data, the data that contains patterns. That data gets inside machine learning logic and algorithm to find a pattern or several patterns. A predictive model is the outcome of the machine learning algorithm process. A model is typically the business logic that identifies the probable patterns with new data. The application is used to supply data to the model to know if the model identifies the known pattern with the new data. In the case that we took, new data could be data of more transactions. Probable patterns mean that a model should come up with predictive patterns to check if the transactions are fraudulent.

Machine Learning and FinTech
FinTech is one of the industries that could be hugely impacted by machine learning and can leverage machine learning technologies to get better predictions and risk analysis in finance applications. Following are five areas where machine learning could impact finance applications and so financial technologies can become smarter to take care of fraud detection, algorithmic trading or portfolio management.

Risk Management
Applying predictive analysis model to the huge amount of real-time data can help the machine learning algorithm to have command over numerous data points. The traditional method of risk management worked on analyzing structured data against some data rules which were very constrained to only structured data. But there is more than 90% of data that is unstructured. Deep learning technology can process unstructured data and does not really depend upon static information coming from loan applications or other financial reports. Predictive analysis can even foresee the loan applicant’s financial status that may be impacted by the current market trends.

Internet Banking Fraud
Another such example could be to detect internet banking fraud. If there is a continuous fraud happening with the fund’s transfer via internet banking and we have the complete data, we could find out the pattern involved. Through this, we can identify where are the loopholes or hack prone areas of the application. So, it’s all about patterns and predicting the results and future based on those patterns. Machine learning plays an important role in data mining, image processing, and language processing. It cannot always provide a correct analysis or cannot always provide an accurate result based on the analysis, but it gives a predictive model based on historical data to make decisions. The more data, the more the result-oriented predictions that can be made.

Sentiment Analysis
One of the areas where machine learning can play an important role is sentiment analysis or news analysis. The futuristic applications on machine learning can no longer depend upon the only data coming from trades and stock prices. As a legacy, the human intuition of financial activities is dependent upon trades and stocks data to discover new trends. The machine learning technology can be evolved to understand social media trends and other information/news trends to do sentiment or news analysis. The algorithms can computationally identify and categorize the opinions or thoughts expressed by the user to make predictive analysis. The more the data the more accurate would be the predictions.

Robo-Advisors
Robo-advisors are a kind of digital platforms to calibrate a financial portfolio. They provide planning services with least manual or human intervention. The users furnish details like their age, current income, and their financial status and expect from Robo-advisors to predict the kind of investment they can make, as per current and futuristic market trends to meet their retirement goals. The advisor processes this request by spreading the investments across financial instruments and asset classes to match the goals of the user. The system works on real-time modification in user’s goals and current market trends and does a predictive analysis to find the best match for the user’s investments. Robo-advisors may in future completely wipe out the human advisors who make money out of these services.

Security
The highest concern for banks and other financial institutions is the security of the user and user’s details, which if leaked could be prone to hacking and eventually resulting in financial losses. The traditional way in which the system works are providing a username and password to the user for secure access and in case of loss of password or recovery of the lost account, few security questions or mobile number validation is needed. Using AI, in the future, one can develop an anomaly detection application that might use biometric data like facial recognition, voice recognition or retina scan. This could only be possible by applying predictive analysis over a huge amount of biometric data to make more accurate predictions by applying repetitive models.

How Can Magic FinServ help?

Magic FinServ is aggressively working on visual analytics and artificial intelligence thereby leveraging the concept of machine learning and transforming the same in the perspective of technology to solve business problems like financial analysis, portfolio management, and risk management. Magic FinServ being a financial services provider can foresee the impact of machine learning and predictive analysis on financial services and financial technologies. The technology business unit of Magic uses technologies like Python, Big Data, Azure Cognitive Services to develop and provide innovative solutions. Data scientists and technical architects at Magic work hand in hand to provide consulting and developing financial technology services having a futuristic approach.

RPA vs Cognitive RPA – Journey of Automation

RPA and Cognitive RPA

Evolution of RPA

IT outsourcing took-off in the early ’90s with broadening globalization driven primarily by labor arbitrage. This was followed by the BPO outsourcing wave in early 2000.

The initial wave of outsourcing delivered over 35% cost savings on an average but continued to stay inefficient due to low productivity & massive demand for constant training due to attrition.

As labor arbitrage became less lucrative with increasing wage & operational cost, automation looked to be a viable alternative for IT & BPO service providers to improve efficiency. This automation was mostly incremental. At the same time, high-cost locations had to compete against their low-cost counterparts and realized that the only way to stay ahead in this race was to reduce human effort.

Robotic Process Automation (RPA) was therefore born with the culmination of these two needs.

What is RPA?

RPA is a software that automates the high volume of repetitive manual tasks. RPA increases operational efficiency and productivity and reduces cost. RPA enables the businesses to configure their own software robots (RPA bots) who can work 24X7 with higher precision and accuracy.

The first generation of RPA started with Programmable RPA solutions, called Doers.

Programmable RPA tools are programmed to work with various systems via screen scraping and integration. It takes the input from other system and determines decisions to drive action. The most repetitive processes are automated by Programmable RPA.

However, Programmable RPA work with structured data and legacy systems. They are highly rule-based without any learning capabilities.

Cognitive Automation is an emerging field which is providing the solution to overcome the limitations of the first-generation RPA system. Cognitive automation is also called “Decision-makers” or “Intelligent Automation”.

Here is a nice diagram published by the Everest group that shows the power of AI/ML in a traditional RPA framework.

Cognitive RPA

Cognitive automation uses artificial intelligence (AI) capabilities like optical character recognition (OCR) or natural language processing (NLP) along with RPA tools to provide end to end automation solutions. It deals with both structured and unstructured data including text-heavy reports. This is probabilistic but can learn the system behavior over time and provide the deterministic solution.

There is another type of RPA solution – “Self-learning solutions” called “Learners”.

Programmable RPA solutions need significant programming effort and technique to enable the interaction with other systems. Self-learning solutions program themselves.

There are various learning methods adopted by RPA tools:

  • It may use historical (when available) and current data, these tools can monitor employee activity over time to understand the tasks. They start completing them after they have gained enough confidence to complete the process.
  • Various tools are used to complete tasks as they are done in the manual ways. Tools learn the necessary activities under the tasks and start automating them. The tool’s capabilities are enhanced by feedback from the operation team and it increases its automation levels.
  • Increasing complexity in the business is the driving factor from rule-based processing to data-driven strategy. Cognitive solutions are helping the business to manage both known and unknown areas, take complex decisions and identify the risk.

As per HfS Research RPA Software and Services is expected to grow to $1.2 billion by 2021 at a compound annual growth rate of 36%. 

Chatbots, Human Agents, Agent assists tools, RPA Robots, Cognitive robots – RPA with ML and AI creates a smart digital workforce and unleash the power of digital transformation

The focus has shifted from efficiency to intelligence in business process operations.

Cognitive solutions are the future of automation…. and data is the key driving factor in this journey.

We, at MagicFinServ, have developed several solutions to help our clients make more out of structured & unstructured data. Our endeavor is to use modern technology stack & frameworks using Blockchain & Machine Learning to deliver higher value out of structured & unstructured data to Enterprise Data Management firms, FinTech & large Buy & sell-side corporations.

Cognitive RPA

Understanding of data and domain is crucial in this process. MagicFinServ has built a strong domain-centric team who understands the complex data of the Capital Markets industry.

The innovative cognitive ecosystem of MagicFinServ is solving the real world problem.

Want to talk about our solution? Please contact us at https://www.magicfinserv.com/.