Earlier the commodity trading advisor is supposed to trade commodities and futures for a managed futures fund. But now selection of investment products is more complex and varied which calls for the need of acute understanding of CTA, of these products. Role of today’s CTA is related to derivative analysis also and hence not only limited role to trading. Analysis is now, the catalyst for the inclusion of value added service to retain customers which includes structured products, risk management and OTC derivatives.

Read more at: http://www.articlesfactory.com/articles/finance/the-role-of-a-cta-commodity-trading-advisor.html

Core Banking Solution (CBS) is networking of branches, which enables customers to operate their accounts, and avail banking services from any branch of the Bank on CBS network, regardless of where he maintains his account. By regional analysis, the market is segmented into North America, Europe, Asia-Pacific and Rest of the World, with North America generating the highest market share owing to higher technology implementation. Due to its capabilities to improve efficiency and better risk management, core banking solutions is growing rapidly. On the basis of solution segment, it offers better customer support service, provides single integrated platform to all the banking channels and lower operational cost, improves efficiency in streamlining process related to customer account management. Read more at: http://www.datasciencecentral.com/profiles/blogs/core-banking-solutions-market-analytical-insights-and-foresight

Big Data is the Buzzword of 21st century as we know it and has been extremely useful in several risk assessment tasks. The effectiveness of Big data on risk management depends on accuracy,consistency ,completeness and timeliness of data. Some most common mistakes made by Big Data experts who are involved in risk management are : Confirmation Bias : It occurs when data scientists use limited data to prove their hypothesis.

Selection Bias : When data is selected subjectively, Analyst comes up with the questions and thus almost picking the data that is going to be received ( Ex : Surveys) 

Outliers : Outliers are often interpreted as normal data

Simpson’s Paradox : When group of data points to one trend, but can reverse when they are combined

Confounding Variables are overlooked

Analyst assume bell curve

Overfitting and Underfitting models

Read more at : http://dataconomy.com/2017/01/7-mistakes-big-data-analysis/

Financial professionals nowadays is managing price risk to handle many challenges like low price, randomness, oversupply and unpredictable swings. You should always be prepared for an unanticipated rise and drop in prices. You need to be foresighted to be good at managing risks. Having no hedging strategy is a risk in itself. Always have a plan according to your needs and experiences in your mind. Heterogeneity is also crucial for overall risk management. Self-made options, currency hedging and structured products are other important solutions to it. Taking an integrated approach is always a good idea. Gone is the time when the companies used to seek help from large banks. A new trend of working along with non-bank organizations to achieve their hedging goals is gaining popularity. Along with regulatory requirements, it ensures to provide a stable balance sheet and reputation. Read more at: http://www.forbes.com/sites/cargill/2016/06/08/manage-risk-with-confidence/#7104ea8f6bd1

Over the past decades, risk management has been transformed in the banking sector after the emergence of global financial crisis. Still we can’t draw a blue print about the risk function. Since customers' experience has become more and more important, banks need to offer real time response to customers and their problems. Similarly, after evolution in technology, it has become important to look upon new risk management techniques. By 2025, manual interventions will be minimizing, hence, there is need to digitalize core processes. Also, more automation and digitalization leads more experimentation with machine learning and advanced analytics.

Better risk reporting requires to adjust with market development and broaden regulations. Different regulatory ratios require to collaborate with balance sheet optimization. With this, high performance data is the urge for high risk performing function. Build a right mix of talent and embed a right culture. To read more, click on the following link: http://www.mckinsey.com/business-functions/risk/our-insights/the-future-of-bank-risk-management

In a dynamic business environment every organization is seeking profit maximization & risk mitigation approach, the article talks about the latter, in uncertain business environment business leaders at time of complex & critical decision making take decision on the basis of intuition (gut feeling) rather than holistic analysis of situation. Even with data backed decisions there is a narrower approach attached in form of point estimates & averages. In this article a new approach Prescriptive Analysis is used where business are simulating probabilities to reduce risk that helps in robust analysis. This approach gives us broader perspectives showing us a range of possibilities & helps in better decision making.Read Full article at :- Target=_blankhttp://www.forbes.com/sites/gartnergroup/2016/03/24/use-prescriptive-analytics-to-reduce-the-risk-of-decisions/#465f9994785b

The challenges faced by the insurance industry are same as other companies. Insurance industry involves generating, storing, and making large & complex sets of data to create efficiencies and improving their bottom line. In a recent survey, 54% of 48 U.S. and P/C insurance executives said they use predictive modelling for underwriting/risk selection, and that usage is expected to grow by 40% over the next two years. Predictive modeling helps in claims triage, underwriting appetite and strategy, market-share analysis, and litigation propensity. Predictive analytics can boost companies' profitability by: 1. Developing a clear analytics roadmap across business units. 

2. Monitoring their outputs against what is happening to avoid the situation where underwriters push back on predictive models.

3. Developing an enterprise-wide model monitoring program to ensure models are recent and recalibrated on a consistent basis. 

4. Looking outside the industry to see how other organizations measure ROI.

For more read the article written by Loren Trimble and Michael Kim(Contributors) : http://ww2.cfo.com/risk-management/2016/03/predictive-modeling-can-make-insurers-better-risk-managers/

Financial managers find it very difficult to reconcile goods received against invoices not received. This issues called 'GR-NI' is time consuming to manage. The problem hence gets reduced to lowest of the priorities and leads to increase of financial liabilities and risk for the business. Without proper automation to monitor the discrepancies, paperwork gets piled up which leads to supplier connections suffering and credits being misused. However, with software based supplier reconciliation process in place, the organization can get rid of this huge task and also improve its audit processes solving several risk issues as they arise. A simple overview of the new 'GR-NI' process says that creation of a database with financial and orders data is to be integrated followed by different other steps. The key elements of this solution are methods to get statements directly into the system regardless of format and source and many others. To maximize the value, operational areas should be looked into that can benefit from better processes. Read more at: http://www.smartdatacollective.com/keith-peterson/329860/using-procurement-analytics-simplify-your-supplier-reconciliation

In the years, succeeding the financial crisis, the banking sector was restructured ranging from changes in regulations to customer service. The major problems that banks are facing include customer dissatisfaction, fraud, increased competition and regulations and all these issues can be solved using Big Data analytics. By leveraging transactional, behavioral and social data, banks can provide a hyper-personalized customer service. Risk management is another area where analytics can be of help. Big Data analytics can detect cybercrimes and predict the location of attack. It can also identify deviation in customer behavior which is indicative of fraud. Big Data technologies can be used to integrate external watch list screening system and unstructured emerging data sources. There is a huge scope for newcomers in Fintech sector to exploit Big Data as they are built keeping analytics in mind. The big banks should adopt new technologies to leverage the wealth of data they possess and maintain a competitive edge. Read more at:https://channels.theinnovationenterprise.com/articles/analytics-in-banking

To stay ahead of new disruptive competitors, banks must understand the value of the data produced from daily transactions across email, mobile and online channels by digitally-led customers and use them to build on their strengths. High street banks and private financial service organizations give customers potential to develop new initiatives with cross-marketing events, loyalty programs. Bank must adopt a mobile-first strategy which engages with customers to maximize longevity. They must deploy mission-critical analytics tools with access to real-time data. Also, more flexible and intelligent OS harnessing the use of big data must be used. By using sophisticated analytics features, banks’ risk management departments can access information on customer-purchasing behavior enabling them to make immediate adjustments to individual customer credit limits or lending rights. Read more about it at: https://channels.theinnovationenterprise.com/articles/7749-how-is-data-remodelling-the-fs-ecosystem 

A company must protect itself from viral attacks and hackers. Such hackers have been in existence for as long as the internet has existed and they make a living out of stealing important business information by selling them in the market to the highest bidders online which can cause terrible damage to the reputation of company in question. The appropriate way to go about this is to install malware removal software’s on all the computers and also ensure protection of their private business and assets. Some other ways to ensure this protection are:

• Sufficient disaster recovery layouts: since virus and malwares on the computer can wipe away important data and ruin the business in seconds, efficient systems for disaster recovery should be in place so that information back up is restored at that very moment after the attack ensuring no loss is incurred. Professionals should be hired for such tasks.

• Effective virus protection should be installed since sensitive information about the business as well as of the employees once leaked can lead to several other problems and regular security checks should be done to ensure everything is updated and new viruses are detected instantly.

• It’s always necessary for the company to regulate the employee’s access to the computers so that information is not mishandled. Every employee must have his own work station and not mingle with others work which ensures no personal information exchange.

Such practices can definitely help the business data stay protected in the long run if followed properly.

Read more at: http://www.smartdatacollective.com/peterdavidson/320351/how-ensure-protection-critical-business-data

When it comes to risk management, it is often observed that the companies either implement blanket management programs applying the same strategies to all employees, or use the "squeaky wheel approach" focusing primarily on at-risk employees. However, both the approaches result in inefficiency. Thus, a strategic employee-specific management program can be adopted to identify the at-risk employees. Such a program monitors the employees for subtle and almost undetectable changes that are indicative of risky behavior and this is where predictive analytics model is of immense help. Predictive modeling enables the manager to identify not only the high-risk employees, but also the cause behind a particular incident.  Predictive modeling is fast becoming an indispensable tool for mitigating risk, retaining top talent, and building long-lasting relationship with the employees. Read More:- http://www.natlawreview.com/article/mitigating-risk-predictive-modeling

Predictive analytics make use of statistical or machine-learning techniques to analyze current and past facts to predict the future. Companies, by using predictive analytics, can make better and decisions at low-cost. Predictive analytics can help companies to get an idea of every possible event, thus allowing for risk management and calculating potential ROI. Using predictive analytics, companies can remove politics from the decision-making process. There is a growing awareness among companies about predictive analytics and companies that adopted this method have reported success and increased ROIs. Optimizing predictive analytics to produce better choices leads to decision modelling. Through decision modelling companies can gain insight into how predictive analytics can add value and how ROI can be measured. Read more at:https://channels.theinnovationenterprise.com/articles/making-faster-decisions-with-predictive-analytics  

The role of risk management has become more pronounced than before and companies are resorting to predictive analytics along with business insights to visualize and manage risk. Predictive analytics has gained immense popularity in the area of risk management due to its ability to identify and predict vulnerabilities, fraud, security breaches and the quality of control systems and governance, as pointed out by Rita Sallam, a research Vice President and analyst. Several firms are utilizing the advanced techniques for data extraction to manage risks. Post data gathering and visualization, firms are able to identify risks and mitigate them. Firms engaging in risk modelling produce impressive returns. Read more at: http://channels.theinnovationenterprise.com/articles/risk-visualisation-and-predictive-analytics-in-risk-management

Analytics has eventually gained importance in the banking sector. From cost optimization, risk balancing to revenue growth, analytics does it all. Operational analytics: reporting, basic forecasting with data and Advanced analytics: model driven, focusing on the predictive aspects- these are used by the banking sector. Slowly customer analytics and risk analytics are also coming into the picture. These help in revenue growth, investment banking, improving customer experience and save the bank from the uncertainties of the market. Analytics is giving the banking sector well defined strategies, changing paradigms with the advancement of technology. With this evolution of analytics, the need for professionals who can bridge the gap between IT and businesses is immediate. Banks are already employing personnel to read into the data offering growth, efficiency and risk management. Read more at:http://www.businessworld.in/news/economy/analytics-&-banking/1719002/page-1.html

Are you planning to invest your wealth and confused to choose which financial product? Did you get your basics right about available ones? What are the things to be taken care of while taking an investment decision? To answer any question of this sort, all you need to do is compare risks, liquidity and earning potential of available products and chose the one that is close to your preference. Risk preference of each investor differs; young investors are more likely to be less risk averse. Also any investor would like to have highest return for the same level of default risk. Money in the bank is very safe as is money invested in bonds which is not the case with money invested in mutual funds.  When it comes to earning potential a careful study of all available options and all possible states of returns is needed. Read more about Investment advices at: http://triblive.com/business/headlines/8394657-74/fund-interest-bonds#axzz3b1MLlddk

Risk analytics is increasingly important for banks as they cope with a complex regulatory and competitive environment. Important technologies and calculation engines are now available that are critically important to the future of banks and the entire industry. At the same time, it is possible to develop an over-reliance on analytics, so a balance needs to be found.

Developing more comprehensive and integrated capabilities is increasingly important. Integrated stress-testing, for example, is an important means by which the science of risk management can be turned into more of an art, such that it can be communicated and appreciated by a wider audience. An effective stress-testing framework encompasses a wider spectrum of macro-economic, social, political and environmental considerations and forecasts and so can help banks avoid the tunnel vision that can prevent them from making good decisions and taking timely action.

Companies are investing in risk analytics and intend to increase those investments, yet the potential return is often stifled by inconsistent or incomplete data. This prevents organizations from generating the insights needed to support a more predictive approach to risk management. To read more: http://www.baselinemag.com/analytics-big-data/banking-on-big-data-and-analytics.html

Different financial services suffer high operational risks when there is market failure and industry transformation. In order to reduce the cost of risk management operations, financial organizations are seeking new models and methodologies. FMEA (Failure Mode and Effect Analysis) and Control charts are two very powerful Six Sigma tools which not only help to identify and prioritize risks but also monitor the risks. To know how FMEA and Control charts help in risk management, please go through the following article written by Abhishek Soni, a certified Six Sigma Black Belt and Project Management Professional.

http://www.isixsigma.com/industries/financial-services/leverage-six-sigma-to-manage-operational-risk-in-financial-services/