Artificial Intelligence Archives - Magic FinServ

Revolutionary use of Smart Contracts to solve your Enterprise Reconciliation and Application Synchronicity Challenges

Until recently, your enterprise may have considered smart contracts as a tool to bridge silos from one organization to another – that is to establish external connectivity over Blockchain. However, what if we proposed applying the same concept so a firm can be instrumental in addressing enterprise-wide data reconciliation and system integration / consolidation challenges to expedite time to market and streamline (i.e internal, regulatory, FP&A, supplier risk) reporting. 

Afterall, about 70-80% of reconciliation activity takes place within the enterprise. The best part? A firm can do this with minimal disruption to its current application suite, operating system and tech stack. We will look at traditional approaches and explain how smart contracts are the way to get started on one of those journeys when one never looks back

To set the stage, let’s cover the self-evident truths. Reconciliation tools are expensive and third party tool implementations typically require multi year (and multi million dollar) investments. Over 70% of Reconciliation requirements are within the Enterprise amongst internal systems. Most reconciliation resolutions start with an unstructured data input (pdf/email/spreadsheet) which requires a manual review/scrubbing to be ingested easily. For mission critical processes, this “readiness of data” lag can result in delays and lost business, backlogs, unjustifiable cost and worst of all, regulatory penalties. 

Magic Finserv proposes a three-fold approach to take on this challenge. 

  1. Data readiness: Tackle the unstructured data solution using AI and ML utilities that can access data sources and ingest them into a structured format. Often Reconcilliation is necessary because of incorrect or incomplete data, ML can anticipate what is wrong / missing from past transactions and remediate. This is the Auto Reconciliation.
  2. Given unstructured data elements may reside in fragmented platforms or organizational silos, the Firm must have an intelligent way of integrating and mutualizing itself with minimal intervention. An ETL or data feed may look appealing initially, however, these are error prone and do not remediate the manual reconciliation tasks for exception management.  Alternatively, a smart contract based approach can streamline your rule-based processes to create a single data source. 
  3. Seamless integration to minimize the disconnect between applications. The goal is to create an environment where reconciliation is no longer required. Ideally.

We have partnered with Digital Asset to outline a solution that brings together an intelligent data extraction tool, a DAML smart contract and a capital markets focused integration partner that will reduce manual end to end reconciliation challenges for the enterprise.

Problem statement & Traditional Approach

Given that most enterprise business processes run through multiple disparate applications with their respective unique databases, it has been proven a monolithic application approach is close to impossible. And not recommended due to issues with a Monolithic application architecture. Traditionally, this challenge has been addressed using integration tools such as an Enterprise Service Bus, SOA, where the business gets consumed in the cycle of data aggregation, cleansing and reconciliation. Each database becomes a virtual pipeline of a business process and an additional staging layer is created to deliver internal/external analytics. In addition, these integration tools are not intelligent as they only capture workflows with adapters (ad hoc business logic) and do not offer privacy restrictions from the outset. 

Solution

The Digital Assets DAML on X initiative extends the concept of the Smart Contract onto multiple platforms including Databases. The DAML on X interfaces with the underlying Databases using standard interfacing protocols, the Smart Contract mutualizes the Data Validation rules as well as the access privileges. Once you create a DAML smart contract, the integrity of the process is built into the code itself, and the DAML runtime makes disparate communication seamless. It is in its DNA to function as a platform independent programming language specifically for multi-party applications.

Without replacing your current architecture such as the ESB, or your institutional vendor management tool of choice, use the DAML runtime to make application communication seamless and have your ESB invoke the necessary elements of your smart contract via exposed APIs .  

Handling Privacy, Entitlements & Identity Management

Every party in the smart contract has a “party ID” plugged in directly with your identity management solution that you are using institutionally. You can even embed “trustless authentication”. 

The idea is that entitlements/rights & obligations are baked directly into the language itself as opposed to a normal business process management tool where you build out your business process and then put the entitlements/ marry them in phase 3 of the process – only to realize that workflow needs to change. 

DAML handles this upfront – all of the authentication is taken care of the persistence layer/IDM that you decide on. The smart contract template represents a data scheme in a database and the Signatories/controllers in our example represent role-level permissioning of who can do what/when and who can see what/when

 The image below shows how the golden source of data is generated.


It is a purpose built product that contains automatic disclosures and privacy parameters out of the box. You don’t need to keep checking your code to see if the guy who is exercising command is actually allowed to see the data or not. All of this is within the scope of the DAML runtime. 

Already kickstarted your enterprise blockchain strategy?

Firstly, Amazing! Second, since DAML Smart contracts can run on databases or distributed ledgers of your choice (Fabric, Corda etc. ), it’s a unique solution that gives you the flexibility to get started with the application building and even change underlying ledgers at any point. You can also Integrate between multiple instances. I.e. If you are running a DAML app on Fabric and another DAML app on corda, both apps can talk to one another. 

The key takeaway here is that most enterprises are held up with determining which ledger meets their needs. With its intuitive business workflow focused approach, developing your DAML applications while you select your ledger fabric can expedite revenue capture, implement consistent enterprise reporting and reduce the burden of reconciliation – the smart contract through to the integration layer is completely portable. 

Optimizing Business Processes for a Post COVID-19 World

COVID 19 and the associated social distancing and quarantine restrictions, has dictated new measures for business standards, forcing companies into a major overhaul in the way they work. Remote Working is just one key part of this change, this impacts workplaces and the global workforce significantly.

This cause-effect relationship is now at the forefront, fundamentally transforming existing business models, business practices, business processes, and supporting structures and technology. According to Gartner, “CIOs can play a key role in this process since digital technologies and capabilities influence every aspect of business models.”

Business process management ( BPM)  was the primary means for investment banks and hedge funds  to make internal workflows efficient. In investment banking, BPM  focused on the automation of operations management by identifying, modeling, analyzing, and subsequently improving business processes.

Most investment firms have some form of BPM for various processes. For instance, compliance processes appear to have some form of software automation in their workflows at most investment banks and hedge funds. This is because banking functions such as compliance, fraud, and risk management exert pressure to develop cost-effective processes. Wherever Automation was not possible,  manual labor-intensive functions were outsourced through KPO’s to comparatively cheaper South-East Asian destinations thereby reducing costs. With COVID-19’s social distancing norms levied, this traditional KPO model to handle Front, Middle, and Back Office processes is cracking up as it relies on several people working together. There is an urgent need to rethink these processes with a fresh vision and build intelligent systems that are remotely accessible, for handling all such processes like KYC, AML, Document Digitization, Data Extraction from Unstructured documents, Contract Management, Trade reconciliation Invoice Processing, Corporate Actions, etc.

Now more than ever, organizations need to embrace agility, flexibility, and transformation. As per KPMG, the modern enterprise must become agile and resilient to master disruption and maintain momentum. Optimizing the operations process can transform the business to support lean initiatives that lead to innovation—an aspect that can no longer be ignored. With the help of cross-functional domain experts, organizations can discover and subsequently eliminate inefficiencies in the operations and business processes by identifying the inconsistencies, redundancies, and gaps that can be streamlined.  Intelligent Workflow initiatives and goals align business improvement with business objectives and visibly reduce the probability of negative ROI and impact on projects and initiatives.

Using new technologies like AI and Machine Learning, organizations can quickly adapt and improve with precision and gain the multi-layered visibility needed to drive change and reach strategic goals across an enterprise. The proper use of Artificial Intelligence can solve business case problems and relieve enterprises from various technology or data chokes. AI techniques can help traditional software perform tasks better over time, thus empowering people to focus their time on complex and highly strategic tasks.

Best-Practices for Adoption of AI-Based BPM Solutions

Before moving into AI-based process automation, a crucial idea for investment banking business leaders to realize is that they need to shift their perspective of emerging technology opportunities. Many AI projects will be deployed before they return the desired result, 100 % of the time.

AI ventures require ample algorithmic tuning, so it can take several months to reach a state of high precision and confidence. This is important because banks, in their core business processes, cannot jump into large AI projects and expect seamless functions across the board straightaway. Any large project would result in a temporary impediment to the specific business process or push it into a downtime before the AI project is complete. 

So bankers need to develop a mentality of try-test-learn-improve while considering AI to gain confidence in data science projects. Also, it is advisable to choose an AI service provider with extensive experience and knowledge of the domain, to achieve desired results. An investment firm should expect to have a prototype solution in the first iteration which they need to improve by incorporating user feedback to correct minor issues to achieve an MVP status. The smaller and shorter projects, that focus on improving a particular sub-process within the entire process workflow are better suited for investment firms. This approach allows small AI teams to develop and deploy projects much faster. Such projects are advisable since they bring a significant positive business impact, while still not hindering the current workflow and process.  

Such attitudinal changes are decisive shifts from the conventional approach to technology that investment banking leaders have taken. This is presumably not something firms can change overnight and requires careful preparation, planning, and a strategy to help the workforce have an incremental improvement approach to business processes. These fundamental shifts demand that leaders prepare, motivate, and equip their workforce to make a change. But leaders must first be prepared themselves before inculcating this approach in their organizations.

Our interactions with CXO’s in the investment banking industry indicate that process optimization applications of AI can bring a  disproportionate benefit in terms of operational efficiency,  sorely needed in these challenging times.  

Magic FinServ offers focussed process optimization solutions for the Financial Services Industry leveraging New Gen Technology such as AI, ML, across hedge funds, asset management, and Fintechs. This allows financial services institutions to translate business strategy and operational objectives into successful enterprise-level changes, thus positively impacting revenues and bottom-line growth. With the relevant domain knowledge of capital markets and technological prowess, our agile team builds customized turnkey solutions that can be deployed quickly and demonstrate returns as early as 2 weeks from the first deployment. Discover the transformation possibilities with our experts on AI solutions for hedge funds and asset managers. 

Write to us mail@magicfinserv.com to book a consultation.

Using AI for Contract Lifecycle Management

Contracting as an activity has been around, ever since the start of the service economy. But despite it being a well-used practice, very few companies have mastered the art of managing contracts efficiently or effectively.  According to a KPMG report, inefficient contracting leads to a loss of 5% to 40% of the value of a given deal in some cases. 

The main challenge facing companies in the financial services industry is the sheer volume of contracts that they have to keep track of; these contracts often lack uniformity and are hard to organize, maintain and update on a regular basis. Manual maintenance of contracts is not only difficult but also cumbersome and prone to multiple forms of errors.  Also, it poses the risk of missing important deadlines or missed scheduled follow-ups, as written in the contract and could potentially lead to expensive repercussions.

Contract management is a way to manage multifarious contracts from vendors, partners, customers, etc. so that data from these contracts can be easily identified, segregated, labeled and extracted to be used in various cases and also updated regularly. 

Recent technological advances in Artificial Intelligence (AI) and Machine Learning, are now helping companies resolve many of the contracting challenges by delivering efficient contract management as a seamless automated solution. 

Benefits of Using AI in the contract management lifecycle

Basic Search

AI can help in enhancing the searchability of the contracts including clauses, dates, notes, comments and, even metadata associated with it. The AI method used for this purpose is called natural language processing(NLP) and the extraction of metadata is done at a granular level to enable the user to search from the vast repository of contracts in an effective manner.

Example: This search function would be extremely useful for the relationship managers/chat-bots to answer any customer queries pertaining to a particular contract. 

Analysis and Diagnostic Search:  AI can be used to proactively identify expiry dates, renewal dates, follow-up dates or low KPI compliance, and then can be used to apply suggestive course of action or flag any alerts. Analytics can further be used to study and predict any kind of risks or non-compliance and therefore send a notification to relevant stakeholders for pending payments or negotiations.

Example: This can be effectively utilised for improving customer satisfaction as well as guide negotiations based on accessible information.

Cognitive Support: AI is highly sought for its predictive intelligence. AI’s predictive capabilities can be used to do an analysis of the existing contracts to understand contract terms & clauses. Its pattern recognition algorithms can identify points of parity, differentiations on pricing, geographic, products & services. Based on the predictive analysis, AI can provide suggestions for inclusion/exclusion of clauses, terms & conditions, etc when authoring new contracts. 

Example : AI systems may automatically predict and suggest clauses pertaining to NDA (non-disclosure agreement) based on the historical contracts that have been previously processed and the events associated with it.

Dynamic Contracts: Advanced AI can be used to build an adaptive dynamic contract. Based on the past data and by taking into account external factors such as market fluctuations, currency exchange, prices, labor rate, changes in laws and regulations, etc, AI algorithms can create a contract. Such a contract would require auditing by an expert but nonetheless would reduce the effort required to generate the contract.

Example: AI can be used to assess existing contracts for making them GDPR (General Data Protection Regulation) compliant. It will insert the relevant data privacy terms and conditions into the contract and subsequently notify the concerned stakeholder about the changes in the contract, so they can be verified.

Challenges in contract management with AI-ML

The use of AI and Machine Learning for contract management is highly promising but it is also challenged by few limitations. 

Machine Learning (ML) is only as effective as the training data that has been used to train the ML algorithms. Therefore, before any AI-ML application is put into practice, an exhaustive dataset of contracts must be developed and then classified, sorted, labeled, and retrieved based on the metadata. This would provide the base, as training data, for AI to build up and therefore put the ‘intelligence’ in the Contract Management process.

For the exhaustive dataset to be developed, all the contract data must be assimilated together. In many organizations, the contracts are still hard copies lying in cabinets. Approximately 10% of written agreements aren’t even traceable. Even when digitized contracts are available, for the AI machine to read these contract’s insights, they must first be in uniform contents. This not only requires scanning of all the documents but also the ability to extract the meaning of the content in the contracts. 

Overcoming the challenges

In order to make the contract portfolios AI-ready, the first step is to  digitize these contract documents. This can be done using OCR (optical character recognition). OCR reads the physical document as a human eye would read it and converts into digitized text which can easily be searched with ML formulas. While it may be too onerous to scan all historical contracts, this purpose can be accomplished by using a CMS (contract management software), which is capable of converting the documents into machine readable filed, thus making a significant data pool. Then AI, can be used to use this data to gain relevant insights. When AI algorithms access huge pools of data, its ability to decipher patterns and provide insights becomes much stronger. The predictive insights can be achieved by incorporating NLP (natural language processing). NLP allows contact groups to identify when contracts have deviated from defined standards. This makes the approval process, negotiation process much faster when the stakeholder is aware of the current contract version deviation from standards. NLP is also used in reporting risk based on language meaning rather than just string matching. For example, identifying those contracts which are about to expire and starting their renewal process.

Conclusion

Potentially, AI in contract management will change the contract management lifecycle to uplevel the strategic role of the contract managers, which would position them in a superior spot while negotiating terms of contracts. It can also help tremendously in strategic planning, risk management, supplier search, and final selections. Thus enhancing the efficiency and effectiveness of category managers. AI innovation continues to play a vital role when contract managers educate themselves and ensure that their contract processes are fully digitized and AI-ready.

Get started with Artificial Intelligence by booking a workshop with us today!

The Underlying Process of Predictive Analysis

Predictive Analysis – What it is?

Whenever you hear the term “Predictive Analysis”, a question pop-ups in mind “Can we predict the future?”. The answer is “no” and the future is still a beautiful mystery as it should be. However, the predictive analysis does forecast the possibility of a happening in the future with an acceptable percentage of deviation from the result. In business terms, predictive analysis is used to examine the historical data and interpret the risk and opportunities for the business by recognizing the trends and behavioral patterns.

Predictive analysis is one of the three forms of data analysis. The other two being descriptive analysis and Prescriptive analysis. The descriptive analysis examines the historical data and evaluates the current metrics to tell if business doing good; predictive analysis predicts the future trends and prescriptive analysis provides a viable solution to a problem and its impact on the future. In simpler words, descriptive analysis is used to identify the problem/scenario, predictive analysis is used to define the likelihood of the problem/scenario and why it could happen; prescriptive analysis is used to understand various solutions/consequences to the problem/scenario for the betterment of the business.

predictive analysis

Predictive Analysis process

The predictive analysis uses multiple variables to define the likelihood of a future event with an acceptable level of reliability. Let’s have a look at the underlying process of Predictive Analysis:

Requirement – Identify what needs to be achieved

This is the pre-step in the process where it is identified what needs to be achieved (requirement) as it paves the ways for data exploration which is the building block of predictive analysis. This explains what a business needs to do more vis-à-vis what is being done today to become more valuable and enhance the brand value. This step defines which type of data is required for the analysis. The analyst could take the help of domain experts to determine the data and its sources.

  1. Clearly state the requirement, goals, and objective.
  2. Identify the constraints and restrictions.
  3. Identify the data set and scope.

Data Collection – Ask the right question

Once you know the sources, the next step comes in to collect the data. One must ask the right questions to collect the data. E.g. to build a predictive model for stock analysis, historic data must contain the prices, volume, etc. but one must also pay attention to how useful the social network analysis would be to discover the behavioral and sentiment patterns.

Data Cleaning – Ensure Consistency

Data could be fetched from multiple sources. Before it could be used, this data needs to be normalized into a consistent format. Normally data cleaning includes –

  1. Normalization
    • a. Convert into a consistent format
  2. Selection
    • a. Search for outliers and anomalies
  3. Pre-Processing
    • a. Search for relationships between variables
    • b. Generalize the data to form group and/or structures
  4. Transformation
    • a. Fill in the missing value

Data Cleaning removes errors and ensures consistency of data. If the data is of high quality, clean and relevant, the results will be proper. This is, in fact, the case of “Garbage In – Garbage out”. Data cleaning can support better analytics as well as all-round business intelligence which can facilitate better decision making and execution.

Data collection and Cleaning as described above needs to ask the right questions. Volume and Variety are two words describing the data collection results, however, there is another important thing which one must focus on is “Data Velocity”. Data is not only required to be quickly acquired but needs to be processed at a good rate for faster results. Some data may have a limited lifetime and will not solve the purpose for a long time and any delay in processing would require acquiring new data.

Analyze the data – Use the correct model

Once we have data, we need to analyze the data to find the hidden patterns and forecast the result. The data should be structured in a way to recognize the patterns to identify future trends.

Predictive analytics encompasses a variety of statistical techniques from traditional methods e.g. data mining, statistics to advance methods like machine learning, artificial intelligence which analyze current and historical data to put a numerical value on the likelihood of a scenario. Traditional methods are normally used where the number of variables is manageable. AI/Machine Learning is used to tackle the situations where there are a large number of variables to be managed. Over the ages computing power of the organization has increased multi-fold which has led to the focus on machine learning and artificial intelligence.

Traditional Methods:

  1. Regression Techniques: Regression is a mathematical technique used to estimate the cause and effect relationship among variables.

In business, key performance indicators (KPIs) are the measure of business and regression techniques could be used to establish the relationship between KPI and variables e.g. economic parameters or internal parameters. Normally 2 types of regression are used to find the probability of occurrence of an event.

  1. Linear Regression
  2. Logistic Regression

A time series is a series of data points indexed or listed or graphed in time order.

Decision Tree

Decision Trees are used to solve classification problems. A Decision Tree determines the predictive value based on a series of questions and conditions.

Advanced Methods – Artificial Intelligence / Machine Learning

Special Purpose Libraries

Nowadays a lot of open frameworks or special purpose libraries are available which could be used to develop a model. Users can use these to perform mathematical computations and see data flow graphs. These libraries can handle everything from pattern recognition, image and video processing and can be run over a wide range of hardware. These libraries could help in

  1. Natural Language Processing (NLP). Natural Language refers to how humans communicate with each other in day to day activities. It could be in words, signs, e-data e.g. emails, social media activity, etc. NLP refers to analyzing this unstructured or semi-structured data.
  2. Computer Vision

Algorithms

Several algorithms which are used in Machine Learning include:

1. Random Forest

Random Forest is one of the popular machine learning algorithm Ensemble Methods. It uses a combination of several decision trees as a base and aggregates the result. These several decision trees use one or more distinct factors to predict the output.

2. Neural Networks (NN)

The approach from NN is to solve the problem in a similar way by machines as the human brain will do. NN is widely used in speech recognition, medical diagnosis, pattern recognition, spell checks, paraphrase detection, etc.

3. K-Means

K-Means is used to solve the clustering problem which finds a fixed number (k) of clusters in a set of data. It is an unsupervised learning algorithm that works itself and has no specific supervision.

Interpret result and decide

Once the data is extracted, cleaned and checked, its time to interpret the results. Predictive analytics has come along a long way and goes beyond suggesting the results/benefits from the predictions. It provides the decision-maker with an answer to the query “Why this will happen”.

Few use cases where predictive analysis could be useful for FinTech business

Compliance – Predictive analysis could be used to detect and prevent trading errors and system oversights. The data could be analyzed to monitor the behavioral pattern and prevent fraud. Predictive analytics in companies could help to conduct better internal audits, identify rules and regulations, improve the accuracy of audit selection thus reducing the fraudulent activities.

Risk Mitigation – Firms could monitor and analyze the operational data to detect the error-prone areas and reduce outages and avoid being late on events thus improving the efficiency.

Improving customer service – Customers have always been the center of business. Online reviews, sentiment analysis, social media data analysis could help the business to understand customer behavior and re-engineer their product with tailored offerings.

Being able to predict how customers, industries, markets, and the economy will behave in certain situations can be incredibly useful for the business. The success depends on choosing the right data set with quality data and defining good models where the algorithms explore the relationships between different data sets to identify the patterns and associations. However, FinTech firms have their own challenges in managing the data caused by data silos and incompatible systems. Data sets are becoming large and it is becoming difficult to analyze for the pattern and managing the risk & return.

Predictive Analysis Challenges

Data Quality / Inaccessible Data

Data Quality is still the foremost challenge faced by the predictive analyst. Poor data will lead to poor results. Good data will help to shape major decision making.

Data Volume / Variety / Velocity

Many problems in Predictive analytics belong to big data category. The volume of data generated by users can run in petabytes and it could challenge the existing computing power. With the increase in Internet penetration and autonomous data capturing, the velocity of data is also increasing at a faster rate. As this increases, traditional methods like regression models become unstable for analysis.

Correct Model

Defining a correct model could be a tricky task especially when much is expected from the model. It must be understood that the same model could be used for different purposes. Sometimes, it does not make sense to create one large complex model. Rather than one single model to cover it all, the model could consist of a large number of smaller models that together could deliver better understanding and predictions.

The right set of people

Data analytics is not a “one-man army” show. It requires a correct blend of domain knowledge with data science knowledge. Data Scientist should be able to ask the correct questions to domain experts in terms of what-if-analysis and domain experts should be able to verify the model with appropriate findings. This is where we at Magic FinServ could bring value to your business. At Magic FinServ we have the right blend of domain expertise as well as data science experts to deliver the intelligence and insights from the data using predictive analytics.

Magic FinServ – Value we bring using Predictive Analysis

Magic Finserv Offerings

Magic FinServ hence has designed a set of offerings specifically designed to solve the unstructured & semi-structured data problem for the financial services industry.

Market Information – Research reports, News, Business and Financial Journals & websites providing Market Information generate massive unstructured data. Magic FinServ provides products & services to tag meta data and extracts valuable and accurate information to help our clients make timely, accurate and informed decisions.

Trade – Trading generates structured data, however, there is huge potential to optimize operations and make automated decisions. Magic FinServ has created tools, using Machine Learning & NLP, to automate several process areas, like trade reconciliations, to help improve the quality of decision making and reduce effort. We estimate that almost 33% effort can be reduced in almost every business process in this space.

Reference data – Reference data is structured and standardized, however, it tends to generate several exceptions that require proactive management. Organizations spend millions every year to run reference data operations. Magic FinServ uses Machine Learning tools to help the operations team reduce the effort in exception management, improve the quality of decision making and create a clean audit trail.

Client/Employee data – Organizations often do not realize how much client sensitive data resides on desktops & laptops. Recent regulations like GDPR make it now binding to check this menace. Most of this data is semi-structured and resides in excels, word documents & PDFs. Magic FinServ offers products & services that help organizations identify the quantum of this risk and then take remedial actions.FacebookLinkedInTwitter

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

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

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 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.

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/.