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Predictive Analytics: What It is and Why It’s Important for Businesses

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Predictive Analytics: What It is and Why It’s Important for Businesses

Did you know that 2.5 quintillion bytes of data are generated on a daily basis? Big data is a valuable asset for companies provided that this data can be utilized to improve their performance. Companies employ predictive analytics to uncover hidden patterns in data and develop quick and efficient strategies that will steer their businesses forward.

IMB Watson is a popular predictive analytics processor that employs natural language processing technology to analyze human speech. IBM Watson can analyze a vast amount of data, often in a fraction of a second, to answer human-framed questions.

What is predictive analytics?

Predictive analytics use a combination of statistical modeling and machine learning techniques to determine the likelihood of future events based on historical data, which can come from structured, unstructured and semi-structured sources. A good example of the use of predictive analytics is the preparation of a credit report of a customer by a financial institution.

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Credit Score:

Financial lenders use predictive analytics to scrutinize relevant data of an individual who has applied for a loan, including data pertaining to the individual’s current assets and debts, his/her employment and history of paying off loans. All this data is analyzed and boiled down to a single value known as credit score. This value represents the lending risk and helps the lender determine a customer’s creditworthiness. The higher the credit score, the more confident is the lender that the customer will fulfill his/her credit obligation.

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Predictive analytics help lenders make quick and efficient decisions, such as accepting or rejecting a customer and increasing or decreasing their loan value. Credit risk modeling training has become extremely important across many sectors, including banking, insurance and retail.

Importance of predictive analytics:

Thanks to the plethora or new age analytics tools and software, predictive analytics make it easier for organizations to plan the future and gain competitive advantage.

Below are some ways in which predictive analytics are used:

  • To predict the probability of certain diseases affecting a specific group of people so that the necessary preventive healthcare measures can be taken.
  • To predict the probability of certain machine parts failing so that preventive maintenance can be administered.
  • To predict the probability of an interruption in a business’s supply chain.
  • To predict customer behavior.
  • To predict safety risks on railroads.
  • To predict traffic flows and the infrastructure requirements of a city.

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How businesses use predictive analytics:

It is imperative for every company to include predictive analytics in their technology portfolio. The major vendors of predictive analytics include SAP, IBM, Oracle, SAS, Information Builders, etc. Their on-premise and cloud-based versions give companies a lot of options to choose their predictive analytics tools from.

On-premise predictive analytics systems are used by companies requiring high level of analytical power and predictive intelligence. These include companies in the drug and pharmaceutical sector; companies working on life science fields like genomics; and research institutes and universities.

Cloud-based versions provide predictive analytics solutions to companies on a per usage or subscription basis. These are highly beneficial for small and medium sized companies where predictive analytics aren’t the core component, but they are still critical for their success and need to be fitted in a stipulated IT budget. Companies can use the ‘’try and buy’’ facility provided by cloud-vendors to test if a particular software is working for them before finalizing a contract.

Companies that lack prior experience in predictive analytics can opt for SaaS (Software as a Service), which are cloud-based solutions with expertise in a specific sector, for example healthcare.

Role of Business Leaders:

Business leaders must be skilled in using the insights provided by predictive analytics to develop strategies that drive their businesses forward. This includes two things; firstly coming up with well-construed questions and secondly identifying the right kind of data to analyze. These will determine whether predictive analytics is working for a company or not.

Companies in all industry verticals are employing predictive analytics to formulate future strategies. As mentioned in a report- ‘’the global market for predictive analytics is projected to grow to $3.6 billion USD by 2020.”

To more about predictive analytics follow Dexlab Analytics– a premier analytics training institute in Gurgaon. Do take a look at their credit risk modeling courses.

 

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A Comprehensive Article on the Trends, Dynamics and Developments of Risk Analytics Market

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Risk analytics makes organizations aware of the potential risks in their businesses. It helps companies make risk-aware decisions and improves their overall business performance. Risk analytics tools help investors get a better return on their capital and minimize the money required to be spent on regulatory compliances. Risk analytics tools aid in the central clearing of over-the-counter (OTC) derivatives.

Classification of Risk Analytics Market

Risk analytics market is divided based on:

  • Component type: Component segment of risk analytics market is further classified based on:
    • Type of solution: Risk analytics software group for regulatory compliance, market risk management, credit risk management, etc. are included in this group.
    • Services: Software services associated with risk analytics software like systems integration and risk evaluation are included in this group.
  • Size of enterprise: Risk analytics market based on size of enterprise is further categorized as:
    • Large organizations
    • Small and medium organizations
  • End-use verticals: Risk analytics market based on end-use vertical is further classified as:
    • BFSI- Banking, financial services and insurance
    • Manufacturing and retail
    • Telecom and IT
    • Government
    • Energy and utility, etc.

Risk analytics is expected to draw large revenues from BFSI sector. Recent times have seen developing countries perform better than the developed economies. This causes currency fluctuations and entails considerable risk. In the face of this current economic climate, BSFI sector is demanding improved risk analytics solution. State-of-art risk analytics tools are an absolute necessity for BSFI sector as they have to spot potential frauds using statistical models.

Main Drivers of Risk Analytics Market

  1. Market risk augmentation owing to:
  • Lack of economic stability
  • Market competitiveness
  1. Stringent regulations and policies are causing a surge in the demand of risk analytics software. Following are some policies responsible for the increased demand:
  • Basel I and II
  • Comprehensive Capital Analysis and Review
  • Dodd-Frank Wall Street Reform
  • Consumer Protection Act (CCAR/DFAST)

Small and medium sized enterprises lack cognizance of risk analytics tools. Moreover, a hefty amount of money is required for the installation of risk analytics tools. These issues are likely to hinder the growth of risk analytics market.

A developed IT sector and authoritative presence of blue chip companies are the key factors boosting the development of risk analytics market. North America is expected to hold majority of the market share in risk analytics market. Significant growth in risk analytics market is likely to occur in the Asia Pacific region. The growing competition in the market and fluctuations in currency will fuel the demand of risk analytic tools.

Major Vendors in Risk Analytics Market

  • IBM Corporation
  • SAP SE
  • Tata Consultancy Services Ltd.
  • SAS Institute
  • Oracle Corporation, etc.

With the rise in global risk, companies have to adopt new approaches to analyze risk. Big data and artificial intelligence are paving the way for the development of revolutionary strategies. CEOs are seeking the valuable input of insurers to curb the threat of cybercrime. Risk teams are turning into strategic advisors.

To know more about risk analytics follow Dexlab Analytics- a premium analytics training institute in Delhi. To gain proficiency in credit management tool, enroll for their credit risk modeling courses.

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How Credit Unions Can Capitalize on Data through Enterprise Integration of Data Analytics

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To get valuable insights from the enormous quantity of data generated, credit unions need to move towards enterprise integration of data. This is a company-wide data democratization process that helps all departments within the credit union to manage and analyze their data. It allows each team member easy-access and proper utilization of relevant data.

However, awareness about the advantages of enterprise-wide data analytics isn’t sufficient for credit unions to deploy this system. Here is a three step guide to help credit unions get smarter in data handling.

Improve the quality of data

A robust and functional customer data set is of foremost importance. Unorganized data will hinder forming correct opinions about customer behavior. The following steps will ensure that relevant data enters the business analytics tools.

  • Integration of various analytics activity- Instead of operating separate analytics software for digital marketing, credit risk analytics, fraud detection and other financial activities, it is better to have a centralized system which integrates these activities. It is helpful for gathering cross-operational cognizance.
  • Experienced analytics vendors should be chosen- Vendors with experience can access a wide range of data. Hence, they can deliver information that is more valuable. They also provide pre-existing integrations.
  • Consider unconventional sources of data- Unstructured data from unconventional sources like social media and third-parties should be valued as it will prove useful in the future.
  • Continuous data cleansing that evolves with time- Clean data is essential for providing correct data. The data should be organized, error-free and formatted.

Data structure customized for credit unions

The business analytics tools for credit unions should perform the following analyses:

  • Analyzing the growth and fall in customers depending on their age, location, branch, products used, etc.
  • Measure the profit through the count of balances
  • Analyze the Performances of the staffs and members in a particular department or branch
  • Sales ratios reporting
  • Age distribution of account holders in a particular geographic location.
  • Perform trend analysis as and when required
  • Analyze satisfaction levels of members
  • Keep track of the transactions performed by members
  • Track the inquires made at call centers and online banking portals
  • Analyze the behavior of self-serve vs. non-self serve users based on different demographics
  • Determine the different types of accounts being opened and figure out the source responsible for the highest transactions.

User-friendly interfaces for manipulating data

Important decisions like growing revenue, mitigating risks and improving customer experience should be based on insights drawn using analytics tools. Hence, accessing the data should be a simple process. These following user-interface features will help make data user-friendly.

Dashboards- Dashboards makes data comprehensible even for non-techies as it makes data visually-pleasing. It provides at-a glance view of the key metrics, like lead generation rates and profitability sliced using demographics. Different datasets can be viewed in one place.

Scorecards- A scorecard is a type of report that compares a person’s performance against his goals. It measures success based on Key Performance Indicators (KPIs) and aids in keeping members accountable.

Automated reports- Primary stakeholders should be provided automated reports via mails on a daily basis so that they have access to all the relevant information.

Data analytics should encompass all departments of a credit union. This will help drawing better insights and improve KPI tracking. Thus, the overall performance of the credit union will become better and more efficient with time.

Technologies that help organizations draw valuable insights from their data are becoming very popular. To know more about these technologies follow Dexlab Analytics- a premier institute providing business analyst training courses in Gurgaon and do take a look at their credit risk modeling training course.

Interested in a career in Data Analyst?

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DexLab Analytics is Heading a Training Session on CRM Using SAS for Wells Fargo & Company, US

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credit risk modelling

We are happy to announce that we have struck gold! Oops, not gold literally, but we are conducting an exhaustive 3-month long training program for the skilled professionals from Wells Fargo & Company, US. It’s a huge opportunity for us, as they have chosen us, out of our tailing contemporaries and hope we do fulfill their expectations!

Wells Fargo & Company is a top notch US MNC in the field of financial service providers. Though headquartered in San Francisco, California and they have several branches throughout the country and abroad. They even have subsidiaries in India, which are functioning well alike. Currently, it is the second-largest bank in home mortgage servicing, deposits and debit cards in the US mainland. Their skilled professionals are adept enough to address complicated finance-induced issues, but they need to be well-trained on tackling Credit Risk Management challenges, as CRM is now the need of the hour.

Our consultants are focused on imparting much in-demand skills on Credit Risk Modeling using SAS to the professionals for the next three months. The total course duration is of 96 hours and the sessions are being conducted online.

 

 

 

 

In this context, the CEO of DexLab Analytics said, “This training session is another milestone for us. At DexLab Analytics, being associated with such a global brand name, Wells Fargo is a matter of great honor and pride, which I share with all my team members. Thanks to their hard work and dedication, we today possess the ability and opportunity to conduct exhaustive training program on Credit Risk Management using SAS for the consultants working at Wells Fargo & Company.”

“The training session starts from today, and will last for three-months. The total session will span over 96 hours. Reinforcing our competitive advantage in the process of development and condoning data analytics skills amongst the data-friendly communities across the globe, we are conducting the entire 3-month session online,” he further added.

Credit Risk Management is crucial to survive in this competitive world. Businesses seek this comprehensive tool to measure risk and formulate the best strategy to be executed in future. Under the umbrella term CRM, Credit Risk Modeling is a robust framework suitable to measure risk associated with traditional crediting products, like credit score, financial letters of credit and etc. Excessive numbers of bad loans are plaguing the economy far and large, and in such situations, Credit Risk Modelling using SAS is the most coveted financial tool to possess to survive in these competitive times.

In the wake of this, DexLab Analytics is all geared up to train the Wells Fargo professionals in the in-demand skill of CRM using SAS to better manage financial and risk related challenges.

To read our Press Release, click:

DexLab Analytics is organizing a Training Program on CRM Using SAS for Wells Fargo Professionals

 

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How Fintechs Help Optimize the Operation of Banking Sector

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How Fintechs Help Optimize the Operation of the Banking Sector

Financial technology- Fintech plays a key role in the rapidly evolving payment scenario. Fintech companies provide improved solutions that affect consumer behavior and facilitate widespread change in the banking sector. Changes in data management pertaining to the payment industry is occurring at a fast pace. Cloud-based solution and API technology (Application Programming Interfaces) has played a major role in boosting the start-up sector of online payment providers like PayPal and Stripe. As cited in a recent PwC report over 95% of traditional bankers are exploring different kinds of partnerships with Fintechs.

 Interpreting consumers’ spending behavior has enhanced payment and data security. Credit risk modeling help card providers identify fraudulent activities. The validity of a transaction can be checked using the GPS system in mobile phones. McKinsey, the consulting firm has identified that the banking sector can benefit the most from the better use of consumer and market data.  Technological advancements have led to the ease of analyzing vast data sets to uncover hidden patterns and trends. This smart data management system helps banks create more efficient and client-centric solutions. This will help banks to optimize their internal system and add value to their business relationship with customers.

Role of Big Data

 Over the past two years, the digital revolution has created more data than in the previous history of humankind. This data has wide-ranging applications such as the banks opening their credit lines to individuals and institutions with lesser-known credit-score and financial history. It provides insurance and healthcare services to the poor. It also forms the backbone of the budding P2P lending industry which is expected to grow at a compound annual growth rate (CAGR) of 48% year-on-year between 2016 and 2024.

The government has channelized the power of digital technologies like big data, cloud computing and advanced analytics to counter frauds and the nuisance of black money. Digital technologies also improve tax administration. Government’s analysis of GST data states that as on December 2017, there were 9.8 million unique GST registrations which are more than the total Indirect Tax registrations under the old system. In future big data will help in promoting financial inclusion which forms the rationale of the digital-first economy drive.

Small is becoming Conventional

Fintechs apart from simplifying daily banking also aids in the financial empowerment of new strata and players. Domains like cyber security, work flow management and smart contracts are gaining momentum across multiple sectors owing to the Fintech revolution. For example workflow management solution for MSMEs (small and medium enterprises) is empowering the industry which contributes to 30% of the country’s GDP. It also helps in the management of business-critical variables such as working capital, payrolls and vendor payments. Fintechs through their foreign exchange and trade solutions minimizes the time taken for banks to processing letter of credit (LC) for exporters. Similarly digitizing trade documents and regulatory agreements is crucial to find a permanent solution for the straggling export sector.

Let’s Take Your Data Dreams to the Next Level

Regulators become Innovators

According to the ‘laissez-faire’ theory in economics, the markets which are the least regulated are in fact the best-regulated. This is owing to the fact that regulations are considered as factors hindering innovations. This in turn leads to inefficient allocation of resources and chokes market-driven growth. But considering India’s evolving financial landscape this adage is fast losing its relevance. This is because regulators are themselves becoming innovators.

The Government of India has taken multiple steps to keep up with the global trend of innovation-driven business ecosystem. Some state-sponsored initiatives to fuel the innovative mindset of today’s generation are Startup India with an initial corpus of Rs 10,000 crore, Smart India Hackathon for crowd-sourcing ideas of specific problem statements, DRDO Cyber Challenge and India Innovation growth Program. This is what enabled the Indian government to declare that ‘young Indians will not be job seekers but job creators’ at the prestigious World Economic Forum (WEF).

From monitoring policies and promoting the ease of business, the role of the government in disruptive innovations has undergone a sea change. The new ecosystem which is fostering innovations is bound to see a plethora of innovations seizing the marketplace in the future. Following are two such steps:

  • IndiaStack is a set of application programming interface (APIs) developed around India’s unique identity project, Aadhaar. It allows governments, businesses, start-ups and developers to utilize a unique digital infrastructure to solve the nation’s problems pertaining to services that are paperless, presence-less and cashless.
  • NITI Ayog, the government’s think tank is developing Indiachain, the country’s largest block chain. Its vision is to reduce frauds, speed up enforcement of contracts, increase transparency of transactions and boost the agricultural economy of the country. There are plans to link Indiachain to IndiaStack and other digital identification databases.

As these initiatives start to unfold, India’s digital-first economy dream will soon be realized.

Advances in technologies like Retail Analytics and Credit Risk Modeling will take the guesswork and habit out of financial decisions. ‘’Learning’’ apps will not only learn the habit of users but will also engage users to improve their spending and saving decisions.

To know more about risk modeling follow Dexlab Analytics and take a look at their credit risk analytics and modeling training course.

Interested in a career in Data Analyst?

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Breaking the Misconceptions: 4 Myths Regarding Data-Driven Financial Marketing

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A majority of low-mid financial services companies toil under the wrong notion that owing to their capacity, size and scope, the complex data-driven marketing tactics are simply out of their reach – this is not true and frankly speaking quite a shame to consider even.

BREAKING THE MISCONCEPTIONS: 4 MYTHS REGARDING DATA-DRIVEN FINANCIAL MARKETING

Over the past decade, the whole concept of data analytics has undergone a massive transformation – the reason being an extensive democratization of marketing tactics. Today’s mid-size financial service providers can easily implement marketing initiatives used by dominant players without any glitch.

Besides, there are several other misconceptions regarding data and its effect on financial marketing that we hear so often and few of them are as follows:

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Myth1 – Legally, banks are only allowed to run broad-based advertising

While it’s partially true that there are certain restrictions on banking institutions when it comes to target consumers, based on income, age, ethnicity and other factors, marketers can still practice an array of tactics, both online and offline.

Marketers can leverage a pool of data for online and offline marketing to formulate data models, keeping in mind the existing customers need and preferences. Once you have an understanding of their online behavior, how they use the data power to carry out transactions, these insights can be applied to attract new customers, who exhibit similar behaviors.

Myth 2 – Data-driven marketing doesn’t bolster customer relationship

It’s a fact, Millennials, especially wants to be aware about financial services and its associated products, and are keen to understand how can banks lend an additional support to their living and social life. Companies can start building relationship based out of it, while implementing data-driven marketing perspective into them.

Myth 3 – You need a huge budget and an encompassing database to drive marketing campaigns

Corporate honchos and digital natives certainly maintain sprawling in-house database to boost marketing activities, but don’t be under the impression that mid-size institutions cannot leverage much from virtual datamart. The impressive SaaS-based solutions houses first-party data, safely and securely and offer you mechanisms that let you integrate with other third-party data, both online and offline.

Datamarts let mid-size marketers achieve a lot of crucial task success. Firstly, you will be able to link online user IDs with offline data – this lets you derive insights about your current customers, including their intents, interests and other details. The most important thing is that it will usher you to build customer models that could target newer customers for your bank.

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Myth 4 – Data-driven marketing is too much time-consuming

A lot of conventional marketers are of the opinion data-driven marketing is a huge concept – time-consuming and labor-intensive. But, that’s nothing but a myth. Hundreds and thousands of mid-size companies develop models, formulate offers and execute campaigns within a 30-day window using a cool datamart.

However, the design and execution part of campaigns need no time, whereas the learning part needs some time. You need to learn how to develop such intricate models, and that’s where time is involved.

To ace on financial models, get hands-on training from credit risk analysis course online – DexLab Analytics offers superior credit risk management courses, along with data analytics, data science, python and R-Programming courses.

In the end, all that matters is prudent marketing campaigns powered by data yields better results than holding onto these misconceptions. So, break the shackles and embrace the power of data analytics.

The article has been sourced from – http://dataconomy.com/2017/08/5-misconceptions-data-driven-marketing

 

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How Credit Risk Modeling Is Used to Assess Credit Quality

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Given the uproar on cyber crimes today, the issue of credit risk modeling is inevitable. Over the last few years, a wide number of globally recognized banks have initiated sophisticated systems to fabricate credit risk arising out of significant corporate details and disclosures. These adroit models are created with a sole intention to aid banks in determining, gauging, amassing and managing risk across encompassing business and product lines.

 

How Credit Risk Modeling Is Used to Assess Credit Quality

 

The more an institute’s portfolio expands better evaluation of individual credits is to be expected. Effective risk identification becomes the key factor to determine company growth. As a result, credit risk modeling backed by statistically-driven models and databases to support large volumes of data needs tends to be the need of the hour. It is defined as the analytical prudence that banks exhibit in order to assess the risk aspect of borrowers. The risk in question is dynamic, due to which the models need to assess the ability of a potential borrower if he can repay the loan along with taking a look at non-financial considerations, like environmental conditions, personality traits, management capabilities and more.

Continue reading “How Credit Risk Modeling Is Used to Assess Credit Quality”

Analyze the Risk of a Borrower with These Sure-fire Credit Risk Analytics Techniques

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It’s a hard but true fact – no more do businesses survive without leverages. In a quest for success and expansion, they need to resort to debt, because equity alone fails to ensure survival. Be it funding a new project, fulfilling working capital requirement or expanding business operations, an organization needs funding for various corporate activities.

 

Analyze the Risk of a Borrower with These Sure-fire Credit Risk Analytics Techniques

 

Talking of India, the credit market scenario in here is not so matured in comparison to other developed countries; hence there exists an excessive dependency level on conventional banking structure. Nevertheless, raising finance from issuance of bonds by companies is also not so rare – majority of companies in need of capital raise money from bonds and shares and this practice is widely prevalent throughout the nation.

Continue reading “Analyze the Risk of a Borrower with These Sure-fire Credit Risk Analytics Techniques”

Credit Risk Modelling: How Indian Fintech Startups Are Hitting a Home Run

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Credit Risk Modelling: How Indian Fintech Startups Are Hitting a Home Run

After scoring high with top notch conglomerates, Indian economy is heating up more than ever – because of flourishing Indian fintech establishments that are popping up here and now.

In this blog, we will take a deeper look down into the mechanism how startups are doing well for themselves in this competitive world from a credit risk perspective. For that, we will dig deep into the personal account of an employee working in one of the notable startups in India, which deals with data analytics product for the financial services industry – what experiences he gathered while working in a startup sector, what advices he would like share and things like that will help us crack this industry better.

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DexLab Analytics offer the best credit risk analysis course.

Pointed things to learn from a fintech startup in India:

Product is king, so is its timing – Never ever compromise with a good product. Similarly, make sure the timing is right too – may be, because you waited too long, you missed the best product. It happens.

Hit the customers right away – Don’t vouch for any product, unless 10 people have validated the product. Allow at least 10 customers to use that product and then sit with them to grab some feedback. Startups work like this, so do you!

Economics is the essence, so do proper homework – Risk and Finance go hand in hand, but are distinct in nature. Get a grip on well-structured financial models – they will help you understand the credit exchange stuffs better. Streaming costs, revenues and growth in a single line will obviously put you in a better position in predicting the impact of credit risk. FYI, credit risk’s impact is endured on not only losses, but on costs too – which is surely a matter of concern.

Teamwork is the best work – Building a potent team is an art. Creating something of your own requires a substantial amount of risk, both personal and professional. Most seasoned consultants coming under a single roof to offer something unique is in itself an exciting idea – startups in India boast of an average age of 25 or 28 years in a particular company. Nevertheless, some companies also excel with a core team whose average experience is that of 10 years – across domains like tech, product, risk, operations, sales and marketing. The figures are interesting, ain’t they?

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Fintech is more finance and less technology – As compared to other industries, fintechs’ operational mode is very different.  Though credit risk and cost management are the founding pillars of a robust fintech business setup, none of them can make up for below-standard quality products. Offering high quality product is of supreme importance for the success of any Fintech, and if you look at fintech companies in the US and Europe you will understand why we are focusing our attention on the quality part.

While we are on the closure, there is still a lot of learning to be done – but we surely believe India is on its way to success and our fintech sector is witnessing a plethora of amazing ideas. Just keep your fingers crossed, and hope our teams pull it off in a snap.

Get credit risk modelling certification from DexLab Analytics today! Their credit risk management courses are intensive, well-researched and are written down, while keeping students’ grasping skills in mind. Go give it a shot!

 


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