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To Be Ahead of the Curve: Banks Must Beef Up Technology

Posted on May 22, 2018May 22, 2018 by Dexlab

To Be Ahead of the Curve: Banks Must Beef Up Technology

Technology is critical. To improve efficiency, reduce costs, stay on the cutting edge over tailing rivals, fulfill customer requirements and initiate a proper risk management process, technology is an incredible tool to possess.

The abovementioned facts received momentum at the SAS Risk & Finance Analytics Roadshow in Lagos, during which it was inferred that the banks nowadays are adapting themselves to regulatory changes, thus reducing costs in no time.

In this context, Charles Nyamuzinga, Senior Business Solutions Manager, Pre-Sales Risk Practice, stated that banks in Africa need to confront with additional challenges, including risk analytics skills gaps, challenges associated with data management and integrating finance and risk management nuances across an organization.

“But, on the positive side, they have started considering technology as a way of eliminating these challenges, and have access to new streams of data that are also helping to advance the financial inclusion mandate,” he noted.

In compliance with global financial norms, African banks should by now be compliant with the new IFRS9 Accounting Standard, which comes with some changes in the way expected credit losses used to be calculated.

“There is also need to start thinking about the new ‘Basel IV’ framework, which impacts on how banks calculate their risk weighted assets, and the amount of capital they need to offset those risks,” he added.

According to Charles, banks are feeling intense regulatory pressure nowadays, while tussling with daily requirements, challenges and questions associated with taking stress tests. The regulators have become severe on stress testing processes, and that may be for good! Besides, banks need to worry about the effect on reputation, capital shortfalls and negative influence on earnings, along with non-compliance penalties.

His concern was thoroughly evident in these statements, “There’s a good chance that banks in Africa could get this wrong if they use disparate and fragmented systems for data management, model building and implementation and reporting – which is often the case – or if they try to do the computations manually.

“The biggest causes of incorrect modeling are data management and quality issues and skills shortages. Banks have to obtain and analyze enormous amounts of detailed data, for example. And, to comply with IFRS 9, banks must look at millions of customers with hundreds of data points.”

In support of the above observations, SAS Sales Manager, West Africa, Babalola Oladokun raised concerns if a bank ends up miscalculating a customer’s credit score, it would result in giving a loan to someone, who for sure won’t be able to repay it. This can have serious implications for IFRS 9expected credit loss calculations. Furthermore, if a bank lacks in capital on hand to offset the loan deficiency, the case will go straightaway to Basel Capital requirements compliance issues.

“Data gathering and manipulation from disparate data sources wastes time and resources that banks could have used to develop new products and find more convenient ways to serve their customers – something their competitors in the FinTech space are very good at,” he noted.

As last thoughts, FinTechs use virgin data streams to draw instant conclusions and fuel decision-making processes for customers. For an example, they base their inferences about granting a loan to someone who doesn’t even have a bank account – surely, this is an innovative way to give non-banking population access into the world of finance.

If finance and big data interests you, we suggest you go through our credit risk management courses in Delhi. DexLab Analytics is not only a trailblazer in credit risk modelling courses, but also a robust platform for training young minds.

This article first appeared in – https://guardian.ng/business-services/technology-crucial-to-tackling-risks-skill-gaps-in-banks

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

Posted on April 30, 2018May 23, 2020 by Dexlab

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.

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.

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.

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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How Fintechs Help Optimize the Operation of Banking Sector

Posted on April 13, 2018April 18, 2018 by Dexlab

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.

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.

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How Credit Risk Modeling Is Used to Assess Credit Quality

Posted on December 30, 2017February 13, 2018 by Dexlab

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.

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”

Banks Merged With Fintech Startups to Perform Better Digitally

Posted on September 13, 2017May 23, 2020 by Dexlab

Axis Bank has acquired FreeCharge, a mobile wallet company opening doors to many such deals in the future. As a consequence, do you think banks and fintech startups have started working towards a common goal?

On some day in the early 2016, Rajiv Anand, the Executive Director of Retail Banking at Axis Bank, asked his team who were hard at work, “Do present-day customers know how a bank would look in the future?”

Continue reading “Banks Merged With Fintech Startups to Perform Better Digitally”

The Basics Of The Banking Business And Lending Risks:

Posted on September 5, 2017August 11, 2018 by Dexlab

Banks, as financial institutions, play an important role in the economic development of a nation. The primary function of banks had been to channelize the funds appropriately and efficiently in the economy. Households deposit cash in the banks, which the latter lends out to those businesses and households who has a requirement for credit. The credit lent out to businesses is known as commercial credit(Asset Backed Loans, Cash flow Loans, Factoring Loans, Franchisee Finance, Equipment Finance) and those lent out to the households is known as retail credit(Credit Cards, Personal Loans, Vehicle Loans, Mortgages etc.). Figure1 below shows the important interlinkages between the banking sector and the different segments of the economy:

Figure 1: Inter Linkages of the Banking Sector with other sectors of the economy

Banks borrow from the low-risk segment (Deposits from household sector) and lend to the high-risk segment (Commercial and retail credit) and the profit from lending is earned through the interest differential between the high risk and the low risk segment. For example: There are 200 customers on the books of Bank XYZ who deposit $1000 each on 1^st January, 2016. These borrowers keep their deposits with the bank for 1 year and do not withdraw their money before that. The bank pays 5% interest on the deposits plus the principal to the depositors after 1 year. On the very same day, an entrepreneur comes asking for a loan of $ 200,000 for financing his business idea. The bank gives away the amount as loan to the entrepreneur at an interest rate of 15% per annum, under the agreement that he would pay back the principal plus the interest on 31^st December, 2016. Therefore, as on 1^st January, 2016 the balance sheet on Bank XYZ is:

Consider two scenarios:

Scenario 1: The Entrepreneur pays off the Principal plus the interest to the bank on 31^st December, 2016

This is a win – win situation for all. The pay-offs were as follows:

Entrepreneur: Met the capital requirements of his business through the funding he obtained from the bank.

Depositors: The depositors got back their principal, with the interest (Total amount = 1000 + 0.05 * 1000 = 1050).

Bank: The bank earned a net profit of 10%. The profit earned by the bank is the Net Interest Income = Interest received – Interest Paid (= $30,000 – $10000 = $20,000).

Credit Risk Analytics and Regulatory Compliance – An Overview – @Dexlabanalytics.

Scenario2: The Entrepreneur defaults on the loan commitment on 31^st December, 2016

This is a drastic situation for the bank!!!! The disaster would spread through the following channel:

Entrepreneur: Defaults on the whole amount lent.

Bank: Does not have funds to pay back to the depositors. Hence, the bank has run into liquidity crisis and hence on the way to collapse!!!!!!

Depositors: Does not get their money back. They lose confidence on the bank.

Only way to save the scene is BAILOUT!!!!!

The Second Scenario highlighted some critical underlying assumptions in the lending process which resulted in the drastic outcomes:

Assumption1: The Entrepreneur (Obligor) was assumed to be a ‘Good’ borrower. No specific screening procedure was used to identify the affordability of the obligor for the loan.

Observation: The sources of borrower and transaction risks associated with an obligor must be duly assessed before lending out credit. A basic tenet of risk management is to ensure that appropriate controls are in place at the acquisition phase so that the affordability and the reliability of the borrower can be assessed appropriately. Accurate appraisal of the sources of an obligor’s origination risk helps in streamlining credit to the better class of applicants.

Assumption2: The entire amount of the deposit was lent out. The bank was over optimistic of the growth opportunities. Under estimation of the risk and over emphasis on growth objectives led to the liquidation of the bank.

Observation: The bank failed to keep back sufficient reserves to fall back up on, in case of defaults. Two extreme lending possibilities for a bank are: a. Bank keeps 100% reserves and lends out 0%, b. Bank keeps 0% and lends out 100%. Under the first extreme, the bank does not grow at all. Under the second extreme (which is the case here!!!) the bank runs a risk of running into liquidation in case of a default. Every bank must solve an optimisation problem between risk and growth opportunities.

The discussion above highlights some important questions on lending and its associated risks:

What are the different types of risks associated with the lending process of a bank?
How can the risk from lending to different types of customers be identified?
How can the adequate amount of capital to be reserved by banks be identified?

The answers to these questions to be discussed in the subsequent blogs.

Stay glued to our site for further details about banking structure and risk modelling. DexLab Analytics offers a unique module on Credit Risk Modelling Using SAS. Contact us today for more details!

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Transformation of Smartphones with AI

Posted on April 18, 2017May 23, 2020 by Dexlab

Once a science fiction fantasy, Artificial Intelligence is today’s resonating reality. People are already relishing myriad advantages through advanced mobile apps and smart-forever smartphones.

Supposedly, smartphones made our lives easier. Not only does it allows us stay in touch with our beloved ones 24/7 but also allow us easy accessibility to a humungous amount of information over the internet, help us reach our designated destinations, play games, watch movies, check mails, and lot more.. And in the thick of all the telltales of new cameras and additional storage, AI is bringing in a poignant change in the realm of smartphone technology, which will impact our lives immensely.

Integrating AI with apps

How would you feel when your phone opens INSTAGRAM, before you tapped on it? You will be elated! Isn’t it? We are already witnessing some basic versions of above-mentioned technology on some phones where the most-used apps pop up on the top of the screen. It’s no more a thing from a science fiction novel; Google Now would know everything about you – the way you use your phone, when you call your home, when you need to tap open a map app, or even the exact moment you feel like taking a photo. No more you have to arrange your home screen icons or click on the apps you need, because whenever you will unlock your phone, the app you want to open will launch automatically.

Mark your steps with AI

On the other hand, if we talk about mapping systems, you must have already come by Apple maps and Google maps that can predict your next whereabouts, based on past searches and destinations put into. In the near future, this technology will get cleverer intellectually. Making decisions based on your preferred routes, the type of public transport you board, how you react when you are stuck in traffic won’t be a tad difficult, provided Google has all the information it needs about you.

Say Hi to a digital assistant

Do you wonder at times, what if your phone becomes your best friend? Though it may sound creepy at first, but this is exactly the way towards which Artificial Intelligence is heading to. Digital assistants will be more like your best buddy who will be beside you on your happiest and worst days. If you feel stressed at work, your digital assistant will know how to uplift your mood or what kind of music to play to make you better..

FELICITATIONS to the Personal assistant app

How about having an inbuilt personal assistant app to do the flight bookings or order some selected items from your shopping cart? Sounds cool! From restaurant bookings and comparing gas-energy prices to sending smart replies, this personal assistant using the bounties of AI excels on a bouquet of jobs.

Fulfilling software programs like Cortana, Siri and Google Now have already started bridging the gap between them and real-life personal assistants. In the future, this gap will further be lessened and these apps will finally be able to do many smart functions.

Get the best credit risk modeling training with our specialists at DexLab Analytics. Credit risk modelling certification course is in great demand now, so come to us and enrol.

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To Be Ahead of the Curve: Banks Must Beef Up Technology

Interested in a career in Data Analyst?